Wearable heart rate monitor

ABSTRACT

A biometric monitoring device is used to determine a user&#39;s heart rate by using a heartbeat waveform sensor and a motion detecting sensor. In some embodiments, the device collects collecting concurrent output data from the heartbeat waveform sensor and output data from the motion detecting sensor, detects a periodic component of the output data from the motion detecting sensor, and uses the periodic component of the output data from the motion detecting sensor to remove a corresponding periodic component from the output data from the heartbeat waveform sensor. From this result, the device may determine and present the user&#39;s heart rate.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/292,673, titled “WEARABLE HEART RATE MONITOR” and filed on May 30,2014 (Attorney Docket No. FTBTP002X1JUS), which is acontinuation-in-part of U.S. patent application Ser. No. 13/924,784,titled “PORTABLE BIOMETRIC MONITORING DEVICES AND METHODS OF OPERATINGSAME” and filed on Jun. 24, 2013 (Attorney Docket No. FTBTP002US), whichclaims benefit of priority under 35 U.S.C. §119(e) to U.S. ProvisionalPatent Application No. 61/662,961, titled “WIRELESS PERSONAL BIOMETRICSMONITOR” and filed on Jun. 22, 2012 (Attorney Docket No. FTBTP002PUS)and 61/752,826, titled “PORTABLE MONITORING DEVICES AND METHODS OFOPERATING SAME” and filed on Jan. 15, 2013 (Attorney Docket No.FTBTP002P2US); this application also claims benefit of priority under 35U.S.C. §119(e) to U.S. Provisional Patent Application No. 61/830,600,titled “PORTABLE MONITORING DEVICES AND METHODS OF OPERATING SAME” andfiled on Jun. 3, 2013 (Attorney Docket No. FTBTP002X1PUS), 61/946,439,titled “HEART RATE DATA COLLECTION” and filed on Feb. 28, 2014 (AttorneyDocket No. FTBTP002X1APUS), 61/955,045, titled “GPS POWER CONSERVATIONUSING ENVIRONMENTAL DATA” and filed on Mar. 18, 2014 (Attorney DocketNo. FTBTP002X1DPUS), 61/973,614, titled “GPS ACCURACY REFINEMENT USINGEXTERNAL SENSORS” and filed on Apr. 1, 2014 (Attorney Docket No.FTBTP002X1EPUS), 62/001,624, titled “FITNESS MONITORING DEVICE WITHALTIMETER” and filed on May 21, 2014 (Attorney Docket No.FTBTP002X1HPUS), and 62/001,585, titled “WEARABLE HEART RATE MONITOR”and filed on May 21, 2014 (Attorney Docket No. FTBTP002X1GPUS), all ofwhich are hereby incorporated by reference herein in their entireties.

BACKGROUND

Recent consumer interest in personal health has led to a variety ofpersonal health monitoring devices being offered on the market. Suchdevices, until recently, tended to be complicated to use and weretypically designed for use with one activity, e.g., bicycle tripcomputers.

Recent advances in sensor, electronics, and power source miniaturizationhave allowed the size of personal health monitoring devices, alsoreferred to herein as “biometric tracking” or “biometric monitoring”devices, to be offered in extremely small sizes that were previouslyimpractical. For example, the Fitbit Ultra is a biometric monitoringdevice that is approximately 2″ long, 0.75″ wide, and 0.5″ deep; it hasa pixelated display, battery, sensors, wireless communicationscapability, power source, and interface button, as well as an integratedclip for attaching the device to a pocket or other portion of clothing,packaged within this small volume.

The disclosure provides methods and devices for activating, in energyefficient ways, HR monitor based on user motion and skin proximity. Thedisclosure also provides methods for operating the LED and photodetector of heart rate monitors to obtain accurate reading of heart ratetailored for different user characteristics such as skin colors.

SUMMARY

One aspect of the disclosure provides methods of determining a user'sheart rate when wearing a biometric monitoring device having a pluralityof sensors including a heartbeat waveform sensor and a motion detectingsensor. The methods may remove motion artifacts from heartbeat waveformsignals when determining a user's heart rate. The methods may becharacterized by the following operations: (a) collecting concurrentoutput data from the heartbeat waveform sensor and output data from themotion detecting sensor, wherein the output data from the heartbeatwaveform sensor provides information about the user's heart rate andwherein the output data from the motion detecting sensor providesinformation about the user's periodic physical movements other thanheartbeats; (b) determining a periodic component of the output data fromthe motion detecting sensor; (c) using the periodic component of theoutput data from the motion detecting sensor to remove a correspondingperiodic component from the output data from the heartbeat waveformsensor; (d) determining the user's heart rate; and (e) presenting theuser's heart rate. In some implementations, the motion detecting sensoris an accelerometer or a gyroscope. In some embodiments, the heartbeatwaveform sensor may be a photoplethysmography sensor or an ECG sensor.

In certain embodiments, the method contains an additional operation ofremoving a harmonic of the corresponding periodic component from theoutput data from the heartbeat waveform sensor. In such embodiments, theperiodic component of the output data from the motion detecting sensormay be a fundamental frequency produced from the user's periodicmovement. In certain embodiments, the operation removing a correspondingperiodic component from the output data from the heartbeat waveformsensor includes applying an adaptive filter to the output data from theheartbeat waveform sensor and the output data from the motion detectingsensor. In certain embodiments, removing a corresponding periodiccomponent from the output data from the heartbeat waveform sensorincludes the operation applying an adaptive filter function to theoutput data from the motion detecting sensor that minimizes thedifference between the output data from the heartbeat waveform sensorand the output data from the motion detecting sensor.

In some implementations, the method includes the following additionaloperations: (i) determining the user's activity level and/or activitytype; and (ii) determining parameters of the function based on theuser's activity level and/or activity type.

In some implementations, the method includes the following additionaloperations: (i) analyzing the output data from the motion detectingsensor to infer that the user is substantially stationary; and (ii)temporarily suspending operation (c).

In some method implementations, the operation collecting the output datafrom the heartbeat waveform sensor includes the following operations:(i) pulsing a light source in the worn biometric monitoring device at afirst frequency; and (ii) detecting light from the light source at thefirst frequency. Pulsing the light source at the first frequency mayinvolve emitting a succession of light pulses of substantially constantintensity.

In certain embodiments, presenting the user's heart rate includespresenting the heart rate on the worn biometric monitoring device. Incertain embodiments, presenting the user's heart rate includespresenting the heart rate on an external device that periodicallycommunicates with the worn biometric monitoring device.

Another aspect of the invention pertains to wearable fitness monitoringdevices designed or configured to remove motion artifacts from heartbeatwaveform signals when determining a user's heart rate. Such devices maybe characterized by the following features: a motion sensor configuredto provide output corresponding to motion by a user wearing the fitnessmonitoring device; a heartbeat waveform sensor; and control logic. Thecontrol logic includes instructions for: (a) collecting concurrentoutput data from the heartbeat waveform sensor and output data from themotion detecting sensor, wherein the output data from the heartbeatwaveform sensor provides information about the user's heart rate andwherein the output data from the motion detecting sensor providesinformation about the user's periodic physical movements other thanheartbeats; (b) determining a periodic component of the output data fromthe motion detecting sensor; (c) using the periodic component of theoutput data from the motion detecting sensor to remove a correspondingperiodic component from the output data from the heartbeat waveformsensor; (d) determining the user's heart rate; and (e) presenting theuser's heart rate.

The control logic is typically, though not necessarily, located on thefitness monitoring device. It may be implemented as hardware, software,firmware, or any combination thereof. The data used by the control logicin executing the instructions described herein may be stored (e.g.,buffered) by associated memory, registers, and the like, which may beentirely resident on the device or partially resident on a pairedsecondary device. Examples of suitable architectures for implementingthe control logic are presented below with reference to, e.g., FIGS.11A-G, 12A-C, 13A-B, and 14A-D.

In some devices, the motion detecting sensor is an accelerometer or agyroscope. In certain embodiments, the device's heartbeat waveformsensor is a photoplethysmographic sensor having (i) a periodic lightsource, (ii) a photo detector positioned to receive periodic lightemitted by the light source after interacting with the user's skin, and(iii) circuitry determining the user's heart rate from output of thephoto detector. In some implementations, the photoplethysmographicsensor includes two periodic light sources straddling the photodetector. In some implementations, the photoplethysmographic sensoradditionally includes a housing having a recess in which the photodetector is disposed. The housing of the photoplethysmographic sensormay have a second recess in which the periodic light source is disposed.In some designs, the housing protrudes at least about 1 mm above a basesurface of the wearable fitness monitoring device arranged to pressagainst the user's skin when worn. Further, the photoplethysmographicsensor further may include a spring configured to resist compressionwhen the protruding housing presses against the user's skin. In certainembodiments, the photoplethysmographic sensor also includes an IML filmover the photo detector and the periodic light source. In certainembodiments, the periodic light source is an LED.

In some embodiments, the device's control logic includes instructionsfor removing a harmonic of the corresponding periodic component from theoutput data from the heartbeat waveform sensor; in such cases theperiodic component of the output data from the motion detecting sensormay be a fundamental frequency produced from the user's periodicmovement.

In some implementations, the instructions for removing a correspondingperiodic component from the output data from the heartbeat waveformsensor include instructions for applying an adaptive filter to theoutput data from the heartbeat waveform sensor and the output data fromthe motion detecting sensor. In some implementations, the instructionsfor removing a corresponding periodic component from the output datafrom the heartbeat waveform sensor include instructions for applying anadaptive filter function to the output data from the motion detectingsensor that minimizes the difference between the output data from theheartbeat waveform sensor and the output data from the motion detectingsensor.

In certain embodiments, the device's control logic additionally includesinstructions for: (i) determining the user's activity level and/oractivity type; and (ii) determining parameters of the function based onthe user's activity level and/or activity type. In certain embodiments,the control logic further comprises instructions for: (i) analyzing theoutput data from the motion detecting sensor to infer that the user issubstantially stationary; and (ii) temporarily suspending execution ofthe instructions of (c).

In some implementations, the instructions for collecting the output datafrom the heartbeat waveform sensor include instructions for: (i) pulsinga light source in the worn biometric monitoring device at a firstfrequency; and (ii) detecting light from the light source at the firstfrequency. The instructions for pulsing the light source at the firstfrequency may include instructions for emitting a succession of lightpulses of substantially constant intensity.

In some cases, the instructions for presenting the user's heart rateinclude instructions for presenting the heart rate on the worn biometricmonitoring device. In some cases, the instructions for presenting theuser's heart rate include instructions for presenting the heart rate onan external device that periodically communicates with the wornbiometric monitoring device.

Another aspect of this disclosure concerns methods of determining auser's heart rate when wearing a worn biometric monitoring deviceincluding a plurality of sensors including a heartbeat waveform sensorand a motion detecting sensor. The methods involve a pre-processingoperation prior to motion compensation. The methods may be characterizedby the following operations: (a) collecting concurrent output data fromthe heartbeat waveform sensor and output data from the motion detectingsensor, wherein the output data from the heart beat waveform sensorprovides information about the user's heart rate and wherein the outputdata from the motion detecting sensor provides information about theuser's periodic physical movements other than heartbeats; (b)determining a periodic component of the output data from the motiondetecting sensor; (c) filtering the output data from the heartbeatwaveform sensor to remove variations that are slow with respect to anexpected heart rate, wherein the filtering produces high pass filteredoutput data from the heartbeat waveform sensor; (d) determining theuser's heart rate; and (e) presenting the user's heart rate. In variousimplementations, the methods include using the periodic component of theoutput data from the motion detecting sensor to remove a correspondingperiodic component from the high pass filtered output data from theheartbeat waveform sensor. The filtering may remove a frequency lessthan about 0.6 Hz (or less than about 0.3 Hz) from the output data fromthe heartbeat waveform sensor.

In some implementations, the motion detecting sensor is an accelerometeror a gyroscope. The heartbeat waveform sensor may be aphotoplethysmography sensor or an ECG sensor.

In certain embodiments, methods include an additional operation ofdetermining the expected heart rate, and from the expected heart rate,setting a high pass frequency for the filtering.

In some cases, determining the expected heart rate includes analyzingthe output data from the motion detecting sensor, which may involveinferring a user activity level and/or detecting an intensity of useractivity or a type of user activity. As an example, detecting the typeof user activity may be detecting running, walking, standing, sitting,or lying down. As a further example, detecting the intensity of useractivity may be detecting running or walking.

In some cases, the periodic movement detected by the motion sensor is awearer's limb movements, which may be, e.g., steps, swim strokes, anklerevolutions while bicycling, and leg movements on cardio machines.

In some method implementations, the operation of collecting the outputdata from the heartbeat waveform sensor includes the followingoperations: (i) pulsing a light source in the worn biometric monitoringdevice at a first frequency; and (ii) detecting light from the lightsource at the first frequency. Pulsing the light source at the firstfrequency may involve emitting a succession of light pulses ofsubstantially constant intensity.

In certain embodiments, presenting the user's heart rate includespresenting the heart rate on the worn biometric monitoring device. Incertain embodiments, presenting the user's heart rate includespresenting the heart rate on an external device that periodicallycommunicates with the worn biometric monitoring device.

In some implementations, a method additional includes the operation fordetermining the user's resting heart rate, and in such cases, thefiltering removes a frequency less than about the frequency of theuser's heart rate. In some cases, determining the user's resting heartrate involves measuring the user's heart rate soon after they wake upand are while still stationary in bed. In some cases, determining theuser's resting heart rate involves measuring the user's average heartrate of the user while the user is sleeping. In some cases, determiningthe user's resting heart rate involves making multiple measurements ofthe user's heart rate when the user is awake and stationary.

Another aspect of the invention pertains to wearable fitness monitoringdevices designed or configured to pre-process heartbeat waveform signalswhen determining a user's heart rate. The wearable fitness monitoringdevices may be characterized by the following features: a motion sensorconfigured to provide output corresponding to motion by a user wearingthe fitness monitoring device; a heartbeat waveform sensor; and controllogic containing instructions for: (a) collecting concurrent output datafrom the heartbeat waveform sensor and output data from the motiondetecting sensor, wherein the output data from the heart beat waveformsensor provides information about the user's heart rate and wherein theoutput data from the motion detecting sensor provides information aboutthe user's periodic physical movements other than heartbeats; (b)determining a periodic component of the output data from the motiondetecting sensor; (c) filtering the output data from the heartbeatwaveform sensor to remove variations that are slow with respect to anexpected heart rate, wherein the filtering produces high pass filteredoutput data from the heartbeat waveform sensor; (d) determining theuser's heart rate; and (e) presenting the user's heart rate.

The control logic is typically, though not necessarily, located on thefitness monitoring device. It may be implemented as hardware, software,firmware, or any combination thereof. The data used by the control logicin executing the instructions described herein may be stored (e.g.,buffered) by associated memory, registers, and the like, which may beentirely resident on the device or partially resident on a pairedsecondary device. Examples of suitable architectures for implementingthe control logic are presented below with reference to, e.g., FIGS.11A-G, 12A-C, 13A-B, and 14A-D.

In some devices, the motion detecting sensor is an accelerometer or agyroscope. In certain embodiments, the device's heartbeat waveformsensor is a photoplethysmographic sensor having (i) a periodic lightsource, (ii) a photo detector positioned to receive periodic lightemitted by the light source after interacting with the user's skin, and(iii) circuitry determining the user's heart rate from output of thephoto detector. In some implementations, the photoplethysmographicsensor includes two periodic light sources straddling the photodetector. In some implementations, the photoplethysmographic sensoradditionally includes a housing having a recess in which the photodetector is disposed. The housing of the photoplethysmographic sensormay have a second recess in which the periodic light source is disposed.In some designs, the housing protrudes at least about 1 mm above a basesurface of the wearable fitness monitoring device arranged to pressagainst the user's skin when worn. Further, the photoplethysmographicsensor further may include a spring configured to resist compressionwhen the protruding housing presses against the user's skin. In certainembodiments, the photoplethysmographic sensor also includes an IML filmover the photo detector and the periodic light source. In certainembodiments, the periodic light source is an LED.

In certain embodiments, the control logic includes instructions forusing the periodic component of the output data from the motiondetecting sensor to remove a corresponding periodic component from thehigh pass filtered output data from the heartbeat waveform sensor. Incertain embodiments, the instructions for filtering include instructionsfor removing a frequency less than about 0.6 Hz (or less than about 0.3Hz) from the output data from the heartbeat waveform sensor.

In certain embodiments, the control logic additionally includesinstructions for determining the expected heart rate, and from theexpected heart rate, setting a high pass frequency for the filtering.

In some cases, the instructions for determining the expected heart rateinclude instructions for analyzing the output data from the motiondetecting sensor. Further, the instructions for analyzing the outputdata from the motion detecting sensor may include instructions forinferring a user activity level. Additionally, the instructions foranalyzing the output data from the motion detection sensor may includeinstructions for detecting an intensity of user activity or a type ofuser activity. In some cases, the instructions for detecting the type ofuser activity include instructions for detecting running, walking,standing, sitting, or lying down. In some cases, the instructions fordetecting the intensity of user activity include instructions fordetecting running or walking. In some implementations, the periodicmovement to be detected by the motion sensor is a wearer's limbmovements.

In some implementations, the instructions for collecting the output datafrom the heartbeat waveform sensor include instructions for: (i) pulsinga light source in the worn biometric monitoring device at a firstfrequency; and (ii) detecting light from the light source at the firstfrequency. The instructions for pulsing the light source at the firstfrequency may include instructions for emitting a succession of lightpulses of substantially constant intensity.

In some cases, the instructions for presenting the user's heart rateinclude instructions for presenting the heart rate on the worn biometricmonitoring device. In some cases, the instructions for presenting theuser's heart rate include instructions for presenting the heart rate onan external device that periodically communicates with the wornbiometric monitoring device.

In certain embodiments, the control logic additionally includesinstructions for determining the user's resting heart rate, and theinstructions for filtering include instructions for removing a frequencyless than about the frequency of the user's heart rate.

The instructions for determining the user's resting heart rate mayinclude instructions for (i) measuring the user's heart rate soon afterthey wake up and are while still stationary in bed, (ii) measuring theuser's average heart rate of the user while the user is sleeping, and/or(iii) making multiple measurements of the user's heart rate when theuser is awake and stationary.

Another aspect of the disclosure provides methods of determining auser's heart rate when wearing a biometric monitoring device having aplurality of sensors including a heartbeat waveform sensor and a motiondetecting sensor. The methods determine heart rate within an expectedrange. The methods may be characterized by the following operations: (a)collecting concurrent output data from the heartbeat waveform sensor andoutput data from the motion detecting sensor, wherein the output datafrom the heartbeat waveform sensor provides information about the user'sheart rate and wherein the output data from the motion detecting sensorprovides information about the user's periodic physical movements otherthan heartbeats; (b) determining a periodic component of the output datafrom the motion detecting sensor; (c) processing the output data fromthe heartbeat waveform sensor to remove the periodic component from theoutput data from the heartbeat waveform sensor; (d) using the outputdata from the motion detecting sensor, determining a range of expectedheart rates; (e) determining the user's heart rate from the output dataproduced in (c), wherein the determined heart rate exists within therange of expected heart rates; and (f) presenting the user's heart rate.In some implementations, the motion detecting sensor is an accelerometeror a gyroscope. In some embodiments, the heartbeat waveform sensor maybe a photoplethysmography sensor or an ECG sensor.

In various embodiments, the operation of determining the range ofexpected heart rates includes the following operations: (i) determiningthe user's heart rate when the output data from the motion detectingsensor indicates that the user is substantially stationary; (ii)determining the user's activity level and/or activity type from theperiodic component of the output data from the motion detecting sensor;and (iii) determining the range of expected heart rates from the user'sheart rate determined in (i) and the user's activity level and/oractivity type determined in (ii). In some implementations, a minimumexpected heart rate in the range of expected heart rates is greater thanthe user's heart rate determined in (i). In some implementations, theoperation of determining the user's activity type includes inferring theactivity type from information contained in the output data from themotion detecting sensor or detecting a manually entered selection ofactivity type. In certain embodiments, determining the range of expectedheart rates includes determining the user's step rate from the outputdata from the motion detecting sensor.

In some cases, the operation of determining the user's heart rateinvolves calculating the spectral density of the output data produced in(c).

In some cases, the periodic movement detected by the motion sensor is awearer's limb movements, which may be, e.g., steps, swim strokes, anklerevolutions while bicycling, and leg movements on cardio machines.

In some method implementations, the operation collecting the output datafrom the heartbeat waveform sensor includes the following operations:(i) pulsing a light source in the worn biometric monitoring device at afirst frequency; and (ii) detecting light from the light source at thefirst frequency. Pulsing the light source at the first frequency mayinvolve emitting a succession of light pulses of substantially constantintensity.

In certain embodiments, presenting the user's heart rate includespresenting the heart rate on the worn biometric monitoring device. Incertain embodiments, presenting the user's heart rate includespresenting the heart rate on an external device that periodicallycommunicates with the worn biometric monitoring device.

Another aspect of the invention pertains to wearable fitness monitoringdevices designed or configured to determine user's heart rate within anexpected range of rates. Such devices may be characterized by thefollowing features: a motion sensor configured to provide outputcorresponding to motion by a user wearing the fitness monitoring device;a heartbeat waveform sensor; and control logic having instructions for:(a) collecting concurrent output data from the heartbeat waveform sensorand output data from the motion detecting sensor, wherein the outputdata from the heartbeat waveform sensor provides information about theuser's heart rate and wherein the output data from the motion detectingsensor provides information about the user's periodic physical movementsother than heartbeats; (b) determining a periodic component of theoutput data from the motion detecting sensor; (c) processing the outputdata from the heartbeat waveform sensor to remove the periodic componentfrom the output data from the heartbeat waveform sensor; (d) using theoutput data from the motion detecting sensor, determining a range ofexpected heart rates; (e) determining the user's heart rate from theoutput data produced in (c), wherein the determined heart rate existswithin the range of expected heart rates; and (f) presenting the user'sheart rate.

The control logic is typically, though not necessarily, located on thefitness monitoring device. It may be implemented as hardware, software,firmware, or any combination thereof. The data used by the control logicin executing the instructions described herein may be stored (e.g.,buffered) by associated memory, registers, and the like, which may beentirely resident on the device or partially resident on a pairedsecondary device. Examples of suitable architectures for implementingthe control logic are presented below with reference to, e.g., FIGS.11A-G, 12A-C, 13A-B, and 14A-D.

In some devices, the motion detecting sensor is an accelerometer or agyroscope. In certain embodiments, the device's heartbeat waveformsensor is a photoplethysmographic sensor having (i) a periodic lightsource, (ii) a photo detector positioned to receive periodic lightemitted by the light source after interacting with the user's skin, and(iii) circuitry determining the user's heart rate from output of thephoto detector. In some implementations, the photoplethysmographicsensor includes two periodic light sources straddling the photodetector. In some implementations, the photoplethysmographic sensoradditionally includes a housing having a recess in which the photodetector is disposed. The housing of the photoplethysmographic sensormay have a second recess in which the periodic light source is disposed.In some designs, the housing protrudes at least about 1 mm above a basesurface of the wearable fitness monitoring device arranged to pressagainst the user's skin when worn. Further, the photoplethysmographicsensor further may include a spring configured to resist compressionwhen the protruding housing presses against the user's skin. In certainembodiments, the photoplethysmographic sensor also includes an IML filmover the photo detector and the periodic light source. In certainembodiments, the periodic light source is an LED.

In certain embodiments, the instructions for determining the range ofexpected heart rates include instructions for: (i) determining theuser's heart rate when the output data from the motion detecting sensorindicates that the user is substantially stationary; (ii) determiningthe user's activity level and/or activity type from the periodiccomponent of the output data from the motion detecting sensor; and (iii)determining the range of expected heart rates from the user's heart ratedetermined in (i) and the user's activity level and/or activity typedetermined in (ii). The minimum expected heart rate in the range ofexpected heart rates may be greater than the user's heart ratedetermined in (i). In some embodiments, the instructions for determiningthe user's activity type comprise instructions for inferring theactivity type from information contained in the output data from themotion detecting sensor or detecting a manually entered selection ofactivity type.

In certain embodiments, the instructions for determining the range ofexpected heart rates include instructions for determining the user'sstep rate from the output data from the motion detecting sensor. In someimplementations, the instructions for determining the user's heart rateinclude instructions for calculating the spectral density of the outputdata produced in (c).

In some implementations, the instructions for collecting the output datafrom the heartbeat waveform sensor include instructions for: (i) pulsinga light source in the worn biometric monitoring device at a firstfrequency; and (ii) detecting light from the light source at the firstfrequency. The instructions for pulsing the light source at the firstfrequency may include instructions for emitting a succession of lightpulses of substantially constant intensity.

In some cases, the instructions for presenting the user's heart rateinclude instructions for presenting the heart rate on the worn biometricmonitoring device. In some cases, the instructions for presenting theuser's heart rate include instructions for presenting the heart rate onan external device that periodically communicates with the wornbiometric monitoring device.

Yet another aspect of the disclosure pertains to methods of determininga user's heart rate when wearing a biometric monitoring device having aplurality of sensors including a heartbeat waveform sensor and a motiondetecting sensor. The methods involve duty cycling the operation of theheartbeat waveform sensor. The methods may be characterized by thefollowing operations: (a) collecting concurrent output data from theheartbeat waveform sensor and output data from the motion detectingsensor, wherein the output data from the heartbeat waveform sensorprovides information about the user's heart rate and wherein the outputdata from the motion detecting sensor provides information about theuser's periodic physical movements other than heartbeats; (b)determining a periodic component of the output data from the motiondetecting sensor; (c) processing the output data from the heartbeatwaveform sensor according to a duty cycle, wherein the processingcomprises removing the periodic component from the output data from theheartbeat waveform sensor; (d) determining the user's heart rate; and(e) presenting the user's heart rate.

In some implementations, the motion detecting sensor is an accelerometeror a gyroscope. The heartbeat waveform sensor may be aphotoplethysmography sensor or an ECG sensor.

In certain embodiments, the duty cycle includes an active phase of about5 to 15 seconds for acquiring collecting output data from the heartbeatwaveform sensor and determining the user's heart rate, followed by aninactive phase. In some implementations, successive active phases areseparated by about 1 to 10 minutes. In some implementations, the activephase is triggered by receiving user input requesting a heart ratemeasurement. In some cases, the heartbeat waveform sensor and outputdata from the motion detecting sensor is collected in (a) for about 5-15seconds during each minute of the duty cycle.

In certain embodiments, the methods additionally include the followingoperations: detecting that the user is attempting to read a heart rate;and temporarily suspending the duty cycling. In some cases, the methodsadditionally include the following operations: analyzing the output datafrom the motion detecting sensor to infer a low activity level; and, inresponse, temporarily suspending the duty cycling.

In some cases, the periodic movement detected by the motion sensor is awearer's limb movements, which may be, e.g., steps, swim strokes, anklerevolutions while bicycling, and leg movements on cardio machines.

In some method implementations, the operation of collecting the outputdata from the heartbeat waveform sensor includes the followingoperations: (i) pulsing a light source in the worn biometric monitoringdevice at a first frequency; and (ii) detecting light from the lightsource at the first frequency. Pulsing the light source at the firstfrequency may involve emitting a succession of light pulses ofsubstantially constant intensity.

In certain embodiments, presenting the user's heart rate includespresenting the heart rate on the worn biometric monitoring device. Incertain embodiments, presenting the user's heart rate includespresenting the heart rate on an external device that periodicallycommunicates with the worn biometric monitoring device.

Another aspect of the invention pertains to wearable fitness monitoringdevices designed or configured to duty cycle when determining a user'sheart rate. The wearable fitness monitoring devices may be characterizedby the following features: a motion sensor configured to provide outputcorresponding to motion by a user wearing the fitness monitoring device;a heartbeat waveform sensor; and control logic comprising instructionsfor: (a) collecting concurrent output data from the heartbeat waveformsensor and output data from the motion detecting sensor, wherein theoutput data from the heartbeat waveform sensor provides informationabout the user's heart rate and wherein the output data from the motiondetecting sensor provides information about the user's periodic physicalmovements other than heartbeats; (b) determining a periodic component ofthe output data from the motion detecting sensor; (c) processing theoutput data from the heartbeat waveform sensor according to a dutycycle, wherein the processing comprises removing the periodic componentfrom the output data from the heartbeat waveform sensor; (d) determiningthe user's heart rate; and (e) presenting the user's heart rate.

The control logic is typically, though not necessarily, located on thefitness monitoring device. It may be implemented as hardware, software,firmware, or any combination thereof. The data used by the control logicin executing the instructions described herein may be stored (e.g.,buffered) by associated memory, registers, and the like, which may beentirely resident on the device or partially resident on a pairedsecondary device. Examples of suitable architectures for implementingthe control logic are presented below with reference to, e.g., FIGS.11A-G, 12A-C, 13A-B, and 14A-D.

In some devices, the motion detecting sensor is an accelerometer or agyroscope. In certain embodiments, the device's heartbeat waveformsensor is a photoplethysmographic sensor having (i) a periodic lightsource, (ii) a photo detector positioned to receive periodic lightemitted by the light source after interacting with the user's skin, and(iii) circuitry determining the user's heart rate from output of thephoto detector. In some implementations, the photoplethysmographicsensor includes two periodic light sources straddling the photodetector. In some implementations, the photoplethysmographic sensoradditionally includes a housing having a recess in which the photodetector is disposed. The housing of the photoplethysmographic sensormay have a second recess in which the periodic light source is disposed.In some designs, the housing protrudes at least about 1 mm above a basesurface of the wearable fitness monitoring device arranged to pressagainst the user's skin when worn. Further, the photoplethysmographicsensor further may include a spring configured to resist compressionwhen the protruding housing presses against the user's skin. In certainembodiments, the photoplethysmographic sensor also includes an IML filmover the photo detector and the periodic light source. In certainembodiments, the periodic light source is an LED.

In certain embodiments, the duty cycle has an active phase of about 5 to15 seconds for acquiring collecting output data from the heartbeatwaveform sensor and determining the user's heart rate, followed by aninactive phase. In some cases, the successive active phases areseparated by about 1 to 10 minutes. In certain implementations, thecontrol logic additionally includes instructions for triggering theactive phase by receiving user input requesting a heart ratemeasurement. In some implementations, the control logic includesinstructions for collecting output data from the heartbeat waveformsensor and output data from the motion detecting sensor in (a) for about5-15 seconds during each minute of the duty cycle.

In certain embodiments, the control logic includes instructions for: (i)detecting that the user is attempting to read a heart rate; and (ii)temporarily suspending the duty cycling. In certain embodiments, thecontrol logic includes instructions for: (i) analyzing the output datafrom the motion detecting sensor to infer a low activity level; and (ii)temporarily suspending the duty cycling.

In some implementations, the instructions for collecting the output datafrom the heartbeat waveform sensor include instructions for: (i) pulsinga light source in the worn biometric monitoring device at a firstfrequency; and (ii) detecting light from the light source at the firstfrequency. The instructions for pulsing the light source at the firstfrequency may include instructions for emitting a succession of lightpulses of substantially constant intensity.

In some cases, the instructions for presenting the user's heart rateinclude instructions for presenting the heart rate on the worn biometricmonitoring device. In some cases, the instructions for presenting theuser's heart rate include instructions for presenting the heart rate onan external device that periodically communicates with the wornbiometric monitoring device.

These and other features of the disclosed embodiments will be presentedin more detail below with reference to the associated drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example portable monitoring device which enablesuser interaction via a user interface.

FIG. 2A illustrates an example portable monitoring device which may besecured to the user through the use of a band.

FIG. 2B provides a view of the example portable monitoring device ofFIG. 2A which shows the skin-facing portion of the device.

FIG. 2C provides a cross-sectional view of the portable monitoringdevice of FIG. 2A.

FIG. 3A provides a cross sectional view of a sensor protrusion of anexample portable monitoring device.

FIG. 3B depicts a cross sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 3A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 3C provides another cross-sectional view of an example PPG sensorimplementation.

FIG. 4A illustrates an example of one potential PPG light source andphotodetector geometry.

FIGS. 4B and 4C illustrate examples of a PPG sensor having aphotodetector and two LED light sources.

FIG. 5 Illustrates an example of an optimized PPG detector that has aprotrusion with curved sides so as not to discomfort the user.

FIG. 6A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 6B illustrates an example of a portable biometric monitoring devicehaving a display and wristband. Additionally, optical PPG (e.g., heartrate) detection sensors and/or emitters may be located on the side ofthe biometric monitoring device. In one embodiment, these may be locatedin side-mounted buttons.

FIG. 7 depicts a user pressing the side of a portable biometricmonitoring device to take a heart rate measurement from a side-mountedoptical heart rate detection sensor. The display of the biometricmonitoring device may show whether or not the heart rate has beendetected and/or display the user's heart rate.

FIG. 8 illustrates functionality of an example biometric monitoringdevice smart alarm feature.

FIG. 9 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing.

FIG. 10 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics.

FIG. 11A illustrates an example block diagram of a PPG sensor which hasa light source, light detector, ADC, processor, DAC/GPIOs, and lightsource intensity and on/off control.

FIG. 11B illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 11A which additionally uses a sample-and-holdcircuit as well as analog signal conditioning.

FIG. 11C illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 11A which additionally uses a sample-and-holdcircuit.

FIG. 11D illustrates an example block diagram of a PPG sensor havingmultiple switchable light sources and detectors, light sourceintensity/on and off control, and signal conditioning circuitry.

FIG. 11E illustrates an example block diagram of a PPG sensor which usessynchronous detection. To perform this type of PPG detection, it has ademodulator.

FIG. 11F illustrates an example block diagram of a PPG sensor which, inaddition to the features of the sensor illustrated in FIG. 11A, has adifferential amplifier.

FIG. 11G illustrates an example block diagram of a PPG sensor which hasthe features of the PPG sensors shown in FIGS. 11A-KKF.

FIG. 12A illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 12B illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, location sensor, altitude sensor, skin conductance/wetsensor and communication circuitry which is connected to a processor.

FIG. 12C illustrates an example of a portable biometric monitoringdevice having physiological sensors, environmental sensors, and locationsensors connected to a processor.

FIG. 13A illustrates an example of the use of a motion signal and anoptical PPG signal to measure a heart rate.

FIG. 13B illustrates another example of the use of a motion signal andan optical PPG signal to measure heart rate.

FIG. 14A illustrates an example of a sensor which has an analogconnection to a sensor processor.

FIG. 14B illustrates an example of a sensor which has an analogconnection to a sensor processor which, in turn, has a digitalconnection to an application processor.

FIG. 14C illustrates an example of a sensor device which has one ormultiple sensors connected to an application processor.

FIG. 14D illustrates an example of a sensor device which has one ormultiple sensors connected to sensor processors which, in turn, areconnected to an application processor.

FIG. 15A illustrates an example of a swim detection algorithm using asequential algorithm flow.

FIG. 15B illustrates an example of a swim detection algorithm which usesa parallel algorithm flow.

FIG. 15C illustrates an example of a swim detection algorithm which usesa hybrid of sequential and parallel algorithm flow.

FIG. 15D illustrates an example of a swim detection algorithm which usesa hybrid of sequential and parallel algorithm flow.

FIG. 16A illustrates an example schematic of a sample-and-hold circuitand differential/instrumentation amplifier which may be used in PPGsensing.

FIG. 16B illustrates an example schematic of a circuit for a PPG sensorusing a controlled current source to offset “bias” current prior to atransimpedance amplifier

FIG. 16C illustrates an example schematic of a circuit for a PPG sensorusing a sample-and-hold circuit for current feedback applied tophotodiode (prior to a transimpedance amplifier).

FIG. 16D illustrates an example schematic of a circuit for a PPG sensorusing a differential/instrumentation amplifier with ambient lightcancellation functionality.

FIG. 16E illustrates an example schematic of a circuit for a PPG sensorusing a photodiode offset current generated dynamically by a DAC.

FIG. 16F illustrates an example schematic of a circuit for a PPG sensorusing a photodiode offset current generated dynamically by a controlledvoltage source.

FIG. 16G illustrates an example schematic of a circuit for a PPG sensorincluding ambient light removal functionality using a “switchedcapacitor” method.

FIG. 16H illustrates an example schematic of a circuit for a PPG sensorthat uses a photodiode offset current generated by a constant currentsource (this may also be done using a constant voltage source and aresistor).

FIG. 16I illustrates an example schematic of a circuit for a PPG sensorthat includes ambient light removal functionality and differencingbetween consecutive samples.

FIG. 16J illustrates an example schematic of a circuit for ambient lightremoval and differencing between consecutive samples.

FIG. 17 presents a block diagram of signal processing logic for applyingmotion compensation to a heartbeat waveform signal in accordance withcertain embodiments.

FIG. 18 presents an example of a frequency domain heartbeat waveformsensor signal that undergoes two passes of adaptive filtering. The upperpanel of the figure shows the unfiltered signal and the lower panelshows the same signal after two passes of adaptive filtering.

FIG. 19 presents a graph of typical heart rates for different useractivity types.

DETAILED DESCRIPTION

This disclosure is directed at biometric monitoring devices (which mayalso be referred to herein and in any references incorporated byreference as “biometric tracking devices,” “personal health monitoringdevices,” “portable monitoring devices,” “portable biometric monitoringdevices,” “biometric monitoring devices,” or the like), which may begenerally described as wearable devices, typically of a small size, thatare designed to be worn relatively continuously by a person. When worn,such biometric monitoring devices gather data regarding activitiesperformed by the wearer or the wearer's physiological state. Such datamay include data representative of the ambient environment around thewearer or the wearer's interaction with the environment, e.g., motiondata regarding the wearer's movements, ambient light, ambient noise, airquality, etc., as well as physiological data obtained by measuringvarious physiological characteristics of the wearer, e.g., heart rate,perspiration levels, etc.

Biometric monitoring devices, as mentioned above, are typically small insize so as to be unobtrusive for the wearer. Fitbit offers severalvarieties of biometric monitoring devices that are all quite small andvery light, e.g., the Fitbit Flex is a wristband with an insertablebiometric monitoring device that is about 0.5″ wide by 1.3″ long by0.25″ thick. Biometric monitoring devices are typically designed to beable to be worn without discomfort for long periods of time and to notinterfere with normal daily activity.

In some cases, a biometric monitoring device may leverage other devicesexternal to the biometric monitoring device, e.g., an external heartrate monitor in the form of an EKG sensor on a chest strap may be usedto obtain heart rate data or a GPS receiver in a smartphone may be usedto obtain position data. In such cases, the biometric monitoring devicemay communicate with these external devices using wired or wirelesscommunications connections. The concepts disclosed and discussed hereinmay be applied to both stand-alone biometric monitoring devices as wellas biometric monitoring devices that leverage sensors or functionalityprovided in external devices, e.g., external sensors, sensors orfunctionality provided by smartphones, etc.

In general, the concepts discussed herein may be implemented instand-alone biometric monitoring devices as well as, when appropriate,biometric monitoring devices that leverage external devices.

It is to be understood that while the concepts and discussion includedherein are presented in the context of biometric monitoring devices,these concepts may also be applied in other contexts as well if theappropriate hardware is available. For example, many modern smartphonesinclude motion sensors, such as accelerometers, that are normallyincluded in biometric monitoring devices, and the concepts discussedherein may, if appropriate hardware is available in a device, beimplemented in that device. In effect, this may be viewed as turning thesmartphone into some form of biometric monitoring device (although onethat is larger than a typical biometric monitoring device and that maynot be worn in the same manner). Such implementations are also to beunderstood to be within the scope of this disclosure.

The functionality discussed herein may be provided using a number ofdifferent approaches. For example, in some implementations a processormay be controlled by computer-executable instructions stored in memoryso as to provide functionality such as is described herein. In otherimplementations, such functionality may be provided in the form of anelectrical circuit. In yet other implementations, such functionality maybe provided by a processor or processors controlled bycomputer-executable instructions stored in a memory coupled with one ormore specially-designed electrical circuits. Various examples ofhardware that may be used to implement the concepts outlined hereininclude, but are not limited to, application specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), andgeneral-purpose microprocessors coupled with memory that storesexecutable instructions for controlling the general-purposemicroprocessors.

Standalone biometric monitoring devices may be provided in a number ofform factors and may be designed to be worn in a variety of ways. Insome implementations, a biometric monitoring device may be designed tobe insertable into a wearable case or into multiple, different wearablecases, e.g., a wristband case, a belt-clip case, a pendant case, a caseconfigured to be attached to a piece of exercise equipment such as abicycle, etc. Such implementations are described in more detail in, forexample, U.S. patent application Ser. No. 14/029,764, filed Sep. 17,2013, which is hereby incorporated by reference for such purpose. Inother implementations, a biometric monitoring device may be designed tobe worn in only one manner, e.g., a biometric monitoring device that isintegrated into a wristband in a non-removable manner may be intended tobe worn only on a person's wrist (or perhaps ankle).

Portable biometric monitoring devices according to embodiments andimplementations described herein may have shapes and sizes adapted forcoupling to (e.g., secured to, worn, borne by, etc.) the body orclothing of a user. An example of a portable biometric monitoringdevices is shown in FIG. 1; the example portable monitoring device mayhave a user interface, processor, biometric sensor(s), memory,environmental sensor(s) and/or a wireless transceiver which maycommunicate with a client and/or server. An example of a wrist-wornportable biometric monitoring device is shown in FIGS. 2A through 2C.This device may have a display, button(s), electronics package, and/oran attachment band. The attachment band may be secured to the userthrough the use of hooks and loops (e.g., Velcro), a clasp, and/or aband having memory of its shape, e.g., through the use of a spring metalband. In FIG. 2B, a sensor protrusion and recess for mating a chargerand/or data transmission cable can be seen. In FIG. 2C, a cross-sectionthrough the electronics package is shown. Of note are the sensorprotrusion, main PCB board, and display.

Portable biometric monitoring devices may collect one or more types ofphysiological and/or environmental data from embedded sensors and/orexternal devices and communicate or relay such information to otherdevices, including devices capable of serving as an Internet-accessibledata sources, thus permitting the collected data to be viewed, forexample, using a web browser or network-based application. For example,while the user is wearing a biometric monitoring device, the biometricmonitoring device may calculate and store the user's step count usingone or more biometric sensors. The biometric monitoring device may thentransmit data representative of the user's step count to an account on aweb service (e.g., www.fitbit.com), computer, mobile phone, or healthstation where the data may be stored, processed, and visualized by theuser. Indeed, the biometric monitoring device may measure or calculate aplurality of other physiological metrics in addition to, or in place of,the user's step count. These include, but are not limited to, energyexpenditure, e.g., calorie burn, floors climbed and/or descended, heartrate, heart rate variability, heart rate recovery, location and/orheading, e.g., through GPS, GLONASS, or a similar system, elevation,ambulatory speed and/or distance traveled, swimming lap count, swimmingstroke type and count detected, bicycle distance and/or speed, bloodpressure, blood glucose, skin conduction, skin and/or body temperature,muscle state measured via electromyography, brain activity as measuredby electroencephalography, weight, body fat, caloric intake, nutritionalintake from food, medication intake, sleep periods, e.g., clock time,sleep phases, sleep quality and/or duration, pH levels, hydrationlevels, respiration rate, and other physiological metrics. The biometricmonitoring device may also measure or calculate metrics related to theenvironment around the user such as barometric pressure, weatherconditions (e.g., temperature, humidity, pollen count, air quality,rain/snow conditions, wind speed), light exposure (e.g., ambient light,UV light exposure, time and/or duration spent in darkness), noiseexposure, radiation exposure, and magnetic field. Furthermore, thebiometric monitoring device or the system collating the data streamsfrom the biometric monitoring device may calculate metrics derived fromsuch data. For example, the device or system may calculate the user'sstress and/or relaxation levels through a combination of heart ratevariability, skin conduction, noise pollution, and sleep quality. Inanother example, the device or system may determine the efficacy of amedical intervention, e.g., medication, through the combination ofmedication intake, sleep data, and/or activity data. In yet anotherexample, the biometric monitoring device or system may determine theefficacy of an allergy medication through the combination of pollendata, medication intake, sleep and/or activity data. These examples areprovided for illustration only and are not intended to be limiting orexhaustive. Further embodiments and implementations of sensor devicesmay be found in U.S. patent application Ser. No. 13/156,304, titled“Portable Biometric Monitoring Devices and Methods of Operating Same”filed Jun. 8, 2011 and U.S. Patent Application 61/680,230, titled“Fitbit Tracker” filed Aug. 6, 2012, which are both hereby incorporatedherein by reference in their entireties.

Physiological Sensors

Biometric monitoring devices as discussed herein may use one, some orall of the following sensors to acquire physiological data, including,but not limited to, the physiological data outlined in the table below.All combinations and permutations of physiological sensors and/orphysiological data are intended to fall within the scope of thisdisclosure. Biometric monitoring devices may include but are not limitedto types of one, some, or all of the sensors specified below for theacquisition of corresponding physiological data; indeed, other type(s)of sensors may also or alternatively be employed to acquire thecorresponding physiological data, and such other types of sensors arealso intended to fall within the scope of the present disclosure.Additionally, the biometric monitoring device may derive thephysiological data from the corresponding sensor output data, but is notlimited to the number or types of physiological data that it couldderive from said sensor.

Physiological Sensors Physiological data acquired Optical ReflectometerHeart Rate, Heart Rate Variability Example Sensors: SpO₂ (Saturation ofPeripheral Light emitter and receiver Oxygen) Multi or single LED andphoto Respiration diode arrangement Stress Wavelength tuned for specificBlood pressure physiological signals Arterial Stiffness Synchronousdetection/amplitude Blood glucose levels modulation Blood volume Heartrate recovery Cardiac health Motion Detector Activity level detectionExample Sensors: Sitting/standing detection Inertial sensors, Gyroscopicsensors and/or Fall detection Accelerometers GPS Skin Temperature StressEMG (eletromyographic sensor) Muscle tension EKG or ECG(electrocardiographic sensor) Heart Rate Example Sensors: Heart RateVariability Single-lead ECG or EKG Heart Rate Recovery Dual-lead ECG orEKG Stress Cardiac health Magnetometer Activity level based on rotationLaser Doppler Power Meter Ultrasonic Sensor Blood flow Audio SensorHeart Rate Heart Rate Variability Heart Rate Recovery Laugh detectionRespiration Respiration type, e.g., snoring, breathing, breathingproblems (such as sleep apnea) User's voice Strain gauge Heart RateExample: Heart Rate Variability In a wrist band Stress Wet/ImmersionSensor Stress Example Sensor: Swimming detection Galvanic skin responseShower detection

In one example embodiment, the biometric monitoring device may includean optical sensor to detect, sense, sample and/or generate data that maybe used to determine information representative of, for example, stress(or level thereof), blood pressure, and/or heart rate of a user. (See,for example, FIGS. 2A through 3C and 11A through KKG). In suchembodiments, the biometric monitoring device may include an opticalsensor having one or more light sources (LED, laser, etc.) to emit oroutput light into the user's body, as well as light detectors(photodiodes, phototransistors, etc.) to sample, measure and/or detect aresponse or reflection of such light from the user's body and providedata used to determine data that is representative of stress (or levelthereof), blood pressure, and/or heart rate of a user (e.g., such as byusing photoplethysmography).

In one example embodiment, a user's heart rate measurement may betriggered by criteria determined by one or more sensors (or processingcircuitry connected to them). For instance, when data from a motionsensor(s) indicates a period of stillness or of little motion, thebiometric monitoring device may trigger, acquire, and/or obtain a heartrate measurement or data. (See, for example, FIGS. 9, 12A, and 12B).

Photoplethysmogram (PPG) signal can be recorded on the body using alight source (e.g., an LED) and a corresponding light detector (e.g., aphotodiode). With each cardiac cycle the heart pumps blood to theperiphery. Even though this pressure pulse is somewhat damped by thetime it reaches the skin, it is enough to distend the arteries andarterioles in the subcutaneous tissue. The change in volume caused bythe pressure pulse is detected by illuminating the skin with the lightfrom a light-emitting diode (LED) and then measuring the amount of lighteither transmitted or reflected to a photodiode.

In certain embodiments, PPG or other technique is used to measure aheartbeat waveform. A “heartbeat waveform” generally refers to avariation of any measured signal caused by or correlated with a user'sheartbeat driven blood flow. In some embodiments, the measured signalrelates to blood circulation caused by the heart pumping blood throughthe circulatory system, which causes cardiovascular driven variations incapillary volume or other parameter. In some embodiments, the heartbeatwaveform is measured by photoplethysmography (PPG). In such embodiments,the heartbeat waveform reflects blood volume changes in capillaries,which correlates with a user's heartbeat and pulse (an arterialpalpation caused by the heartbeat). In some embodiments, the measuredsignal relates to muscular activities of the heart orelectrocardiographic signals. In some embodiments, the cardiacactivities or signals can be measured by ECG to obtain the heartbeatwaveform. A heartbeat waveform represents information for one or morecardiac cycles, which corresponds to a complete heartbeat from itsgeneration to the beginning of the next beat. The frequency of thecardiac cycle is described by the heart rate, which is typicallyexpressed as beats per minute. A heartbeat waveform typically includesinformation about various stages of a heartbeat, e.g., amplitude,frequency, and/or shape of waveform over one or more cycles. In manyembodiments, a heartbeat waveform is used to obtain a user's heart rate.

This PPG signal can thus be used to estimate the heart rate of the user.PPG reflects a combination of the underlying change in blood volume dueto heart beats as well as the change in optical properties of theunderlying tissue and the device—skin interface due to subject motion.Typically, the human heart rate is estimated in the range 40-240 beatsper min (bpm).

FIG. 12A illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 12B illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, location sensor, altitude sensor, skin conductance/wetsensor and communication circuitry which is connected to a processor.

In one embodiment, when the motion sensor(s) indicate user activity ormotion (for example, motion that is not suitable or optimum to trigger,acquire, and/or obtain desired heart rate measurement or data (forexample, data used to determine a user's resting heart rate)), thebiometric monitoring device and/or the sensor(s) employed to acquireand/or obtain a desired heart rate measurement or data may be placed in,or remain in, a low power state. Since heart rate measurements takenduring motion may be less reliable and may be corrupted by motionartifacts, it may be desirable to decrease the frequency with whichheart rate data samples are collected (thus decreasing power usage) whenthe biometric monitoring device is in motion.

In another embodiment, a biometric monitoring device may employ data(for example, from one or more motion sensors) indicative of useractivity or motion to adjust or modify characteristics of triggering,acquiring, and/or obtaining desired heart rate measurements or data (forexample, to improve robustness to motion artifact). For instance, if thebiometric monitoring device receives data indicative of user activity ormotion, the biometric monitoring device may adjust or modify thesampling rate and/or resolution mode of sensors used to acquire heartrate data (for example, where the amount of user motion exceeds acertain threshold, the biometric monitoring device may increase thesampling rate and/or increase the sampling resolution mode of sensorsemployed to acquire heart rate measurement or data.) Moreover, thebiometric monitoring device may adjust or modify the sampling rateand/or resolution mode of the motion sensor(s) during such periods ofuser activity or motion (for example, periods where the amount of usermotion exceeds a certain threshold). In this way, when the biometricmonitoring device determines or detects such user activity or motion,the biometric monitoring device may place the motion sensor(s) into ahigher sampling rate and/or higher sampling resolution mode to, forexample, enable more accurate adaptive filtering of the heart ratesignal. (See, for example, FIG. 9).

FIG. 9 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing. In the casewhere there is motion detected (e.g., through the use of anaccelerometer), the user may be considered by the biometric monitoringdevice to be “active” and high-sampling-rate heart rate detection mayoccur to reduce motion artifacts in the heart rate measurement. Thisdata may be saved and/or displayed. In the case that the user isdetermined by the biometric monitoring device to not be moving (or to berelatively sedentary), low-sampling-rate heart rate detection (whichdoes not consume as much power) may be adequate to measure a heart rateand may thus be used.

Notably, where a biometric monitoring device employs optical techniquesto acquire heart rate measurements or data, e.g., by usingphotoplethysmography, a motion signal may be employed to determine orestablish a particular approach or technique to data acquisition ormeasurement by the heartbeat waveform sensor (e.g., synchronousdetection rather than a non-amplitude-modulated approach) and/oranalysis thereof. (See, for example, FIG. 11E). In this way, the datawhich is indicative of the amount of user motion or activity may causethe biometric monitoring device to establish or adjust the type ortechnique of data acquisition or measurement used by an opticalheartbeat waveform sensor or sensors.

For example, in one embodiment, a biometric monitoring device (orheart-rate measurement technique as disclosed herein may adjust and/orreduce the sampling rate of optical heart rate sampling when motiondetector circuitry detects or determines that the biometric monitoringdevice wearer's motion is below a threshold (for example, if thebiometric monitoring device determines the user is sedentary or asleep).(See, for example, FIG. 9). In this way, the biometric monitoring devicemay control its power consumption. For example, the biometric monitoringdevice may reduce power consumption by reducing the sensor samplingrate—for instance, the biometric monitoring device may sample the heartrate (via the heartbeat waveform sensor) once every 10 minutes, or 10seconds out of every 1 minute. Notably, the biometric monitoring devicemay, in addition thereto or in lieu thereof, control power consumptionvia controlling data processing circuitry analysis and/or data analysistechniques in accordance with motion detection. As such, the motion ofthe user may impact the heart rate data acquisition parameters and/ordata analysis or processing thereof.

Motion Artifact Suppression in Heartbeat Waveform Sensors

As discussed above, the raw heartbeat waveform signal measured by a PPGsensor may be improved by using one or more algorithms to remove motionartifacts. In certain embodiments, data from a motion sensor is employedto gauge a user's motion and an adaptive filter is employed to removethe motion artifact from the heart rate signal when the user's motion isperiodic. Movement of the user (for determining motion artifacts) may bemeasured using sensors including, but not limited to, accelerometers,gyroscopes, proximity detectors, magnetometers, etc. The goal of suchalgorithms is to remove components of the PPG signal attributable tomovement (movement artifacts) using the movement signal captured fromthe other sensors as a guide. In one embodiment the movement artifactsin the PPG signal may be removed using an adaptive filter based on ahybrid Kalman filter and a least mean square filter or a recursive leastsquares filter. The heart rate or other feature of a heartbeat waveformmay then be extracted from the cleaned/filtered signal using a peakcounting algorithm or a power spectral density estimation algorithm.Alternatively, a Kalman filter or particle filter may be used to removesuch movement artifacts.

Another approach that may be used to calculate the heart rate frequencyis to create a model of the heart rate signal as Y=Y_(dc)+Σa_(k)*coskθ+b_(k)*sin kθ, where k is the order of harmonic components, and θ is amodel parameter for heart rate. This model may then be fit to the signalusing either an extended Kalman filter or a particle filter. This modelexploits the fact that the signal is not sinusoidal so contains powerboth at the fundamental harmonic as well as multiple additionalharmonics.

Alternately, the signal may be modeled asY=Y_(dc)+Σa_(k)*sin(k*w_(motion)t+θ)+Σb_(k)*sin(k*w_(HR)t+Ø), wherew_(motion) is estimated directly from the accelerometer signal (oranother motion sensor signal).

Various techniques may be employed to improve motion compensation. Someof these techniques are presented in the following sub-sections.

High Band Pass Filter

PPG signals tend to have slow variations due to causes other than heartbeats, for example respiration, movement of blood into or out of themeasurement region, user motions that change the orientation of themonitoring device. Removing these slow variations by high pass filteringthe PPG signal before applying the adaptive filter may improve theperformance of the adaptive filter. Therefore, certain embodimentsprovide a high pass filter placed in front of the adaptive filter. SeeFIG. 17. In some implementations, the cutoff frequency of the high passfilter is positioned as high as possible, but no higher than the lowestheart rate expected (which is around 40 bpm for typical applications),so the high pass filter cutoff may be placed somewhere between about 0.3and 0.6 Hz.

In some implementations, the high pass cutoff frequency is chosen to beapproximately the user's previously detected resting heart rate, whichmay be determined by a variety of techniques. These include at least thefollowing. (1) Measuring a user's heart rate soon after she wakes up andis still stationary in bed. As described elsewhere, the biometricmonitoring device may employ other algorithms to automatically detectwhen the user is asleep/awake. (2) Measuring the average heart rate ofthe user while the user is sleeping. As an example, a user's restingheart rate may be calculated from sleeping heart rate using an equationsuch as resting HR=sleeping HR/0.83. (3) Measuring the heart rate of theuser when the user is awake and stationary. Taking a few of thesemeasurements during the day and calculating the Nth percentile of thelowest of these measurements (e.g., N=10 or 5 or 2) provides a goodapproximation of resting heart rate. The method of approximating restingheart rate during all day activities can further be used to compute auser's resting heart rate at different locations. For example, thebiometric monitoring device can compute a user's resting heart rate athome and at work. In addition to facilitating use in a high pass filter,such information can show trends in home and work resting heart rateover time.

High band pass filter frequency may be set based on the user's detectedactivity level. In such embodiments, the lowest expected heart ratediffers based on activity; for example most users will have a heart rateof higher than about 50 bpm when walking or running. Therefore, incertain implementations, the PPG processing logic dynamically adjuststhe frequency cutoff of the high pass filter by inferring the activityof the user using, e.g., inertial sensors such as accelerometers. As theuser's level of activity increases, as inferred by motion interpretinglogic, the high pass filter cutoff may be adjusted upward accordingly.In certain embodiments, when the processing logic determines that theuser is engaged in walking, it increases high pass filter cutoff inproportion to the user's step rate, elevation climbed, etc. An exampleof the basis for such adjustments can be seen by the variation in anormal user's heart rate between running and walking as shown in FIG.19.

Create Spectrogram Heart Rate Monitor Output (Time Domain->FrequencyDomain)

At an appropriate time in the signal processing, the processing logictransforms the time domain signal to a frequency domain signal, whichcan be represented as frequency versus time. FIG. 18 presents examplesof the frequency domain signal. While the processing logic may performthe transformation using a Fast Fourier Transform, other techniques maybe suitable. Examples include wavelet transforms and cepstraltransforms.

Motion Compensation Process

The heartbeat waveform analysis logic may track the user's motion, asdetected by the device, and apply an adaptive filter to remove or reducethe motion artifact from the heart rate signal when the user's motion isperiodic. In certain embodiments, the motion artifact is measured usingan accelerometer, a gyroscope, or an accelerometer and a gyroscope.

In certain embodiments, the adaptive filter is applied to the heartbeatwaveform data twice in series. In other word, the output of a first passof the adaptive filter is input for the second pass of the adaptivefilter. Although typically unnecessary, the logic may run the adaptivefilter more than two times. This approach may be employed to remove boththe fundamental frequency and the higher harmonics of the motionartifact, as measured using, e.g., an accelerometer.

On the first pass, the filter typically removes the fundamentalfrequency of the motion artifact, and on the second pass, it removes asecond harmonic (or other lower power harmonic). For example, the usermay be walking at nominal rate of 1 Hz to produce an approximately 1 Hzmotion artifact in the heartbeat waveform sensor signal. The first passthrough the adaptive filter may remove this artifact, but not its firstharmonic at nominally 2 Hz, which may interfere with processing theheartbeat waveform signal. Passing the “cleaned” heartbeat waveformsignal through the adaptive filter a second time may remove the secondharmonic of the motion artifact to further clean the signal. In someimplementations, no further cleaning is needed, as higher orderharmonics are of low power compared the heart rate signal and/or atfrequencies far removed from the heart rate signal.

FIG. 18 presents an example of a frequency domain heartbeat waveformsensor signal that undergoes two passes of adaptive filtering. The upperpanel of the figure shows the unfiltered signal and the lower panelshows the same signal after two passes of adaptive filtering. Note thatthe motion artifacts of both higher and lower frequencies than the trueheart rate signal are removed.

Identify Heartbeat Waveform Track

A heartbeat waveform track is a trace of substantially continuous points(with few or no jump discontinuities) in frequency vs. time for theprocessed heartbeat waveform sensor output data. The points of the tracemay have a relatively constant power density.

In certain embodiments, after removing the motion artifact using theadaptive filter, the power spectral density of the signal is calculated.The peak with the highest amplitude can be chosen as the most likelyestimate of the current heart rate. Alternately, techniques like dynamicprogramming or multi target tracking can be used to estimate the heartrate ‘track’ in the spectrogram in the presence of other tracks causeddue to motion and its higher harmonics. In all approaches, one or moreof the following techniques may be implemented to improve performance:

1. The minimum heart rate expected for a user is related to the steprate or other cadence measurement of a user who is walking or running.By measuring the step rate of a user with, e.g., an accelerometer, aheart rate monitor can set a lower bound on the expected heart rate ofthe user. This serves to remove many motion artifacts related peakswhich typically occur at lower frequencies. A similar technique can beapplied to rates of other user activities such as swim strokes, bicyclepeddling cadence, and rates of other activities described herein.

2. The minimum heart rate expected for a user who is biking is relatedto the speed and incline at which the user is biking. By measuring thespeed with a cadence sensor, GPS or other speed measurement sensor, andinclination with an altimeter, GPS or other height measuring sensor, aheart rate monitor can set a lower bound on the expected heart rate ofthe user. This serves to remove many motion artifacts which typicallyhappen at lower frequencies.

3. Lower bounds on the expected heart rate can also be calculated basedon different features in the accelerometer signal like the total power,standard deviation of one or more axes, power in a particular frequencyband etc. Higher total accelerometer power or higher standard deviationof one or more axes are typically related to a person engaged instrenuous activity which leads to a higher heart rate.

4. The minimum heart rate of a user walking, running or biking is alsohigher than the sedentary heart rate of the user. This sedentary heartrate can be calculated when the user is stationary or when he or she issleeping and may be used as a lower bound on the heart rate, alone or inaddition to another mechanism for determining a lower bound (e.g., thelowest heart rate expected for the user's current activity mode).

5. The activity mode of the user like running, walking and biking caneither be automatically calculated using other sensors on the devicelike inertial sensors, or can be manually entered by the user.

Determine Quality of the Heart Rate Estimate

In certain embodiments, the signal processing logic calculates thequality of the heart rate estimate by measuring the magnitude of thepower spectral density at the output for the heartbeat trace compared tothe background power. This monitor may then provide this to the user asfeedback. The feedback can take various forms. In one example, itpresents a confidence regarding how much the heart rate estimate can betrusted. In another example, it provides instructions for improving thegeneration of future heart rate measurements. For example, it maypresent information on whether the user needs to physically adjust theplacement and tightness of the device to achieve a better signal.Further details are presented in the discussion of FIG. 17.

Example Architecture for Motion Compensation in a Heartbeat WaveformSensor

FIG. 17 presents an example architecture (and associated process flow)for a variety of motion compensating heartbeat waveform sensor. In thedepicted example, the signal processing logic receives two inputsignals: (1) heartbeat waveform signals from a PPG sensor 1703, forexample, and (2) motion detection sensor 1705 output. The raw outputfrom a heartbeat waveform sensor may be sampled at a defined frequencyand the samples may be buffered for processing together. Similarly, themotion signal may be sampled and stored. In certain embodiments, theheartbeat waveform sensor and/or the motion detection sensor outputs aresampled at a rate of about 20 to 200 Hz or about 20 Hz to 60 Hz. Incertain embodiments, the heartbeat waveform sensor and motion detectionsensor outputs are sampled at the same frequency. In one example, thetwo signals are sample at 25 Hz. Regardless of the sampling rates, theoutputs are stored for a limited time in buffers 1707 and 1709.

In certain embodiments such as the one depicted in FIG. 17, one or moreadditional inputs are provided to the motion compensating logic. In someimplementations, an additional input is a user activity level oractivity type, as inferred from output data from the motion sensor orother sensors, or detected from a manual setting by the user. Outputfrom device sensors such as motion detecting sensors may be employed forthis purpose. In the architecture depicted in FIG. 17, the signalprocessing logic employs the user's current state (e.g., sedentary,participating in activity 1, participating in activity 2, etc.—see block1733) and the user's activity rate (see block 1735), sometimes referredto as cadence. The user's activity type/state is optional in someembodiments. Examples of activity types include, in addition tosedentary, walking, running, biking, etc. In some implementations, thedevice includes a periodic motion rate calculator that calculates theuser's step rate or other periodic motion rate. Periodic motion ratesare used at various stages in the process.

The buffered heart rate and motion sensor outputs may be separatelyprocessed in some embodiments. In other embodiments, they are processedtogether early in the motion compensation process. In the designsembodied by the architecture of FIG. 17, both buffered output samplesare subjected to the same “pre-processing” operations (see block 1711).Examples of such pre-processing operations include band pass filtering,high pass filtering, low pass filtering, and denoising. As an example,both types of sample are filtered by a filter, which may remove slowvarying components of the outputs, which introduce signal processingchallenges as explained above. In the example, both signals aresubjected to the same pre-processing but they are not combined.

In some embodiments, pre-processing block 1711 dynamically chooses acutoff high pass frequency based on user activity level or activity typeas described above. Inputs from 1733 and 1735 may be used for thispurpose. As an example, a high pass cut off of 0.7 Hz, corresponding to42 beats per minute, may be appropriate for some user activities but notothers. Therefore, in some implementations, the algorithm attempts todetermine the user's current activity and choose an appropriate cut offfrequency for that activity. In one example, the algorithm determinesthe user's step rate from the motion sensor output data and uses thestep rate to choose an appropriate high pass cut off frequency.

In the embodiment depicted in FIG. 17, the signal processing logicbuffers the pre-processed heartbeat waveform sensor signal in a buffer1713 and buffers the pre-processed motion sensor signal in a buffer1715. In certain embodiments, each buffer holds data samples from about1 to 40 seconds or from about 5 to 20 seconds. In one example, thebuffer holds about 10 seconds of data.

In certain implementations, the signal processing logic includesdifferent channels for processing heartbeat waveform data based ondifferent characteristics of the data. Such characteristics may dependon inferred or user-set characteristics of the user, which, in somecases, may be varied dynamically. Examples of such characteristicsinclude user activity type, user activity level (e.g., cadence or steprate), time of day, time of the week, location, etc. In the embodimentdepicted in FIG. 17, the characteristic is activity type and/or level(e.g., periodic motion rate). As shown, an activity discrimination logicblock 1717 receives as inputs the user's state (from block 1733) and theuser's cadence (from block 1735). The activity discrimination block maybe viewed as a form of multiplexer. The signal processing logic usesthis information, or other information in other embodiments, to choose achannel. In some implementations, the logic chooses a channel based onuser input from a device's user interface, which may be provided on thebiometric monitoring device or a secondary device. If the device detectsthat the user entered a particular mode like “biking,” the processinglogic may decide that channel for activities including biking should beused to process the heartbeat waveform. In the depicted embodiment,there are three channels: a “stationary” channel, a “first activity”channel, and a “second activity” channel. Examples of activityprocessing channels include a channel for walking/running, one forworking out, and/or channels for more specific types of working out suchas biking, swimming, elliptical training, rock climbing, tennis, etc. Ofcourse, the stationary mode channel is inferred when sensor outputsuggests that the user is generally inactive. Typically, there may betwo or more channels. In certain embodiments, there are 2-20 channels,or 2-10 channels, or 2-5 channels.

In each of the channels, the signal processing logic converts the timedomain signals acquired from the sensors to frequency domain signals. Inthe depicted embodiment, this is accomplished at a logic block 1719 forthe stationary mode channel, a logic block 1723 for the first activitychannel, and a logic block 1725 for the second activity channel.

As an example, a stationary mode channel may be implemented as follows.First, the stationary mode is determined based on the user's motion. Ifthe user has not moved or moved only slightly during a recent timewindow (e.g., 5 minutes), the processing logic (e.g., block 1717) deemsthe user stationary for purposes of further processing of the bufferedsensor output data. In this channel, the logic assumes that there is nomotion artifact, so the processing is simplified in comparison toprocessing other channels.

Some or all of the following operations may be performed in thestationary channel as well as one or more other “activity” channels:

A. Perform a Fast Fourier Transform or other time to frequency domaintransform. This may provide an initial “estimate” of the user's heartrate. Before this transform, all processing is conducted in the timedomain. As mentioned, block 1719 of FIG. 17 performs this operation inthe stationary channel.

B. Identify a heart rate track in the spectral representation of thefiltered output data from the heartbeat waveform sensor. This processmay be performed as described above. In certain embodiments, this isperformed by excluding putative heart rate tracks below an expectedlevel for the user's activity type. In the case of the stationary modechannel, this level is about 30 beats per minute.

C. Optionally smooth the estimated time varying heart rate to provide asmoothed output heart rate. Smoothing may be performed using manydifferent smoothing filters known to those of skill in the art. In theembodiment depicted in FIG. 17, an optional smoothing logic block 1727smoothes the time varying heart rate signal using input from blocks 1733(user state) and 1735 (user cadence).

D. In some implementations, the signal processing logic assesses the“confidence” of the heart rate value determined from motion compensatedprocess. In the embodiments of FIG. 17, a logic block 1729 makes thisassessment. As explained elsewhere herein, the confidence may bedetermined by any of various techniques. In one approach, it usesinformation contained in a heartbeat waveform sensor; for example, thesignal-to-noise ratio of the sensor's output data. When a lowsignal-to-noise ratio is detected, the confidence is low; when a highsignal to noise ratio is detected, the confidence is higher.

E. Typically, the signal processing logic completes its processing ofbuffered heartbeat waveform data by presenting its calculated heart rateto the user. See logic block 1731 in FIG. 17. In certain embodiments,the signal processing logic presents the heart rate value to the useralong with the calculated confidence in the presented heart rate. Insome cases, the logic presents the heart rate value along with arecommendation for adjusting the device position to improve confidencein subsequent heart rate calculations. As examples, the logic maysuggest wearing the device tighter or wearing it higher on the user'sforearm.

The heart rate information may be presented to the user visually,audibly, tactilely, etc. The information may be provided via thebiometric monitoring device and/or an associated secondary device suchas a paired phone, table, computer, or other processing device. In someimplementations, the information is provided via a “one touch” heartrate monitor design. See e.g., U.S. patent application Ser. No.14/154,009, filed Jan. 1, 2014, and incorporated herein by reference inits entirety.

Turning to the first activity mode channel, an example of the processingfor this mode may be implemented as follows. Initially, the firstactivity mode is determined based on the user's motion. Many criteriamay be employed to determine the user's activity mode. At a minimum, thedevices should receive sensor output suggesting that the user is engagedin some activity and is not sedentary. In a typical implementation, amotion sensor output shows that the user or a user's limb is moving at areasonable rate. Alternatively, the processing logic may rely on manualinput from the user indicating that the user is engaged in the firstactivity. Regardless of how the signal processing logic (e.g., block1717) determines that the user is participating in the first activity,it begins processing of the buffered output data via the first activitychannel. In this channel, the logic assumes that there is a motionartifact that should be removed or reduced before calculating the user'sheart rate. Otherwise, the channel processing may be implementedsimilarly to that of the stationary channel.

The first activity channel may employ an adaptive filter to remove orreduce the motion artifacts from the time domain signal. See block 1721in the embodiments of FIG. 17. In certain implementations, the adaptivefilter attempts to predict the heartbeat waveform sensor output datafrom the motion sensor output data. This determines the motion artifactin the heartbeat waveform sensor output because the motion artifact isthe only component common to the two output signals. Stated another way,the adaptive filter subtracts the motion artifact from the heart rateoutput signal to provide “cleaned” heart rate output data. Examples ofadaptive filtering that may be employed include least mean squarefiltering and recursive least squares filtering. Optionally, theprocessing logic passes the cleaned heartbeat waveform output signalthrough the adaptive filter a second time, as explained above. Examplesof other motion compensation algorithms that may be employed includedynamic programming, and multi-target tracking. As mentioned, the motioncompensation process may be employed two more times on the heartbeatwaveform signal. The motion sensor output used in motion compensationmay represent any of a variety of user physical motions. Examplesinclude periodic limb movements such as steps, swim strokes, anklerevolutions while bicycling, and leg movements on cardio machines suchas stationary bicycles, treadmills, and elliptical machines.

Further processing of the heartbeat waveform signal in the firstactivity channel may proceed generally as described above for thestationary channel. However, identifying the heartbeat track may employmore sophisticated processing. The following are operations that may beemployed in first activity channel.

A. Employ an FFT or other frequency domain conversion technique to getan “estimate” of the user's heart rate from the time domain data outputfrom the adaptive filter. See block 1723 for location of this operationin the embodiments of FIG. 17.

B. Identify a heart rate track in the spectral representation of thefiltered output data from the heartbeat waveform sensor. See block 1724in the embodiments of FIG. 17. In certain embodiments, this is performedby excluding putative heart rate tracks below an expected level for theuser's activity type. In the case of the stationary mode channel, thislevel is about 40 beats per minute. In some implementations, the logiccreates or employs a band of expected heart rate frequencies for eachactivity type and/or activity level, and, for any given activity type,excludes all potential heart rates outside the band. Examples ofinformation that may be used to define the bands include inferred useractivity type, step rate, and defined sensor outputs (e.g., GPS speedwhile biking, altimeter change for bicycling). In certain embodiments,the minimum heart rate band for any activity is greater than the user'ssedentary heart rate (previously determined).

C. Smooth the estimate to get a smoothed output heart rate.

D. Assess the “confidence” of the output heart rate. This processoptionally uses the user's motion as determined by a different process.It may optionally use the signal-to-noise ratio of the motion sensoroutput.

E. Present the calculated heart rate to the user. This information maybe presented along with the confidence level and/or a recommendation foradjusting device position to improve confidence. Information may bepresented to the user visually, audibly, tactilely, etc. The informationmay be provided via the biometric monitoring device and/or an associatedsecondary device such as a paired phone, table, computer, or otherprocessing device. In some implementations, the information is providedvia a “one touch” heart rate monitor design. See e.g., U.S. patentapplication Ser. No. 14/154,009, filed Jan. 1, 2014, and incorporatedherein by reference in its entirety.

As mentioned, additional activity channels may be employed, such as thechannel associated with block 1725 in FIG. 17. In many implementations,the additional channels employ variations on the first activity channeldepicted in FIG. 17. The variations may include the location of theboundaries on the minimum and maximum calculated heart rate and howaggressively the logic filters the signal from the heartbeat waveformsensor.

The control logic used to implement the heartbeat waveform processingdescribed herein typically, though not necessarily, located on thefitness monitoring device. It may be implemented as hardware, software,firmware, or any combination thereof. It may be said that theinstructions are provided by “programming”. Such programming isunderstood to include logic of any form including hard coded logic indigital signal processors and other devices which have specificalgorithms implemented as hardware. Programming is also understood toinclude software or firmware instructions that may be executed on ageneral purpose processor. The data used by the control logic inexecuting the instructions described herein may be stored (e.g.,buffered) by associated memory, registers, and the like, which may beentirely resident on the device or partially resident on a pairedsecondary device. Examples of suitable architectures for implementingthe control logic are presented below with reference to, e.g., FIGS.11A-G, 12A-C, 13A-B, and 14A-D.

In some embodiments, the control logic includes a processor, chip, card,or board, or a combination of these, which includes logic for performingone or more control functions. In some embodiments, instructions forcontrolling the heartbeat waveform sensor are stored on a memory deviceassociated with the fitness monitoring device or are provided over anetwork. Examples of suitable memory devices include semiconductormemory, magnetic memory, optical memory, and the like. The computerprogram code for controlling the heartbeat waveform sensor can bewritten in any conventional computer readable programming language suchas assembly language, C, and the like. Compiled object code or script isexecuted by a processor to perform the tasks identified in the program.

Duty Cycling in Heartbeat Waveform Sensors

Device power can be saved by duty cycling the heartbeat waveform sensorduring regular activities. The power savings may be achieved by turningoff a PPG sensor's light source and/or light detector, and by reducingoperation of the signal processing algorithm. The processing logic mayduty cycle the sensor light source to correspond with the duty cycle forprocessing data.

Based on the determined activity state, the heart rate logic determineshow frequently to generate a new heart rate reading. When in an activitystate associated with high physical activity, the logic may determinethe heart rate frequently (e.g., every second). However, when notengaged in a strenuous physical activity, it generates a new heart ratereading less frequently. Since a heart rate estimate requirescalculation of a power spectral density, which requires PPG data for aminimum duration (e.g., about 5-15 seconds of data), the duty cyclelogic should ensure that the sensor is turned on for the minimum time.In some implementations, duty cycling may be implemented as follows:

1. Once sufficient samples are obtained to calculate a heart rateestimate, the sensor (light source and light detector) may be turned offuntil the next sample is requested.

2. In the case where the user is actively looking at the estimate on ascreen, duty cycling may be turned off and the user sees instantaneousupdates in heart rate.

3. If the user is engaged in physical activity like running or bikingwhere high resolution (e.g., 1 second resolution) is desired, again dutycycling may be turned off.

4. When a user is not operating in a way requiring frequent updates, thedevice logic can switch back to low power mode and can measure heartrate at a low frequency, such as once per minute. Even when the logicdoes not receive input suggesting that the user is interested inreceiving updates, infrequent heart rate measurements may be taken foruse in long term trends (e.g., day long).

5. In certain implementations, a device employing duty cycling mayreduce the power consumed by an LED light source by 75% and the powerconsumed by the signal processing algorithm by 90%.

Ambient Light and Skin Color

Ambient light and skin color may make it difficult to extract a user'sheart rate from a PPG signal. The effect of ambient light may be reducedby subtracting a value of the received detected light signal when thePPG light source is off from the value of the received detected lightsignal when the PPG light source is on (assuming that both signals areobtained in close temporal proximity to each other).

The effect of skin color may be reduced by changing the intensity of thePPG light source, the wavelength of the light emitted from the lightsource, and/or by using the ratio or difference of received signalcorresponding to two different wavelengths. Skin color may be determinedby using user input (e.g. the user entering their skin color), an imageof the person's face, etc., and may then subsequently be used tocalibrate the algorithm, light source brightness, light sourcewavelength, and the receiver gain. The effect of skin color (andtightness with which the user is wearing the device) on the raw PPGsignal may also be measured by sending in a signal of known amplitude tothe light source(s) and then measuring the received signal from thephotodetector(s). Such a signal may be sent for a prolonged period oftime (so as to capture data through multiple expected heart beats) andthen averaged to produce a steady-state data set that is not heart-ratedependent. This amplitude may then be compared to a set of values storedin a table to determine algorithm calibration, transmitter amplitude andthe receiver gain.

Heart Rate Estimate Improvement Using Heuristics

After getting an initial estimate of the heart rate (e.g., by peakcounting or a power spectral density estimation), it may be useful toapply bounds on the allowable rates for heart rate. These bounds may beoptimized on a per-user basis since each user will have a unique heartrate profile. For example, the sedentary rate of each user may beestimated when they are stationary and this may be used as a lower boundwhen the user is walking. Similarly, half the frequency of walking ascalculated from the pedometer may serve as a good lower bound for theexpected heart rate.

The heart rate algorithm may be tailored for each user and may learn theheart rate profile of the user and adapt to the user's behaviors and/orcharacteristics so as to perform better with time. For example, thealgorithm may set bounds on the heart rate expected during a particularphysical activity or rate of walking based on historical data from thatuser. This may help provide better results when the heart rate data iscorrupted by noise and/or motion artifacts.

HR Quality Metric

In another example embodiment, a signal quality metric of the heartrate/PPG signal may be used to provide a quantification of theaccuracy/precision of the signal being generated. Depending on thevalues of this metric, the algorithm that determines what the user'sheart rate (or other PPG-derived metric such as respiration) is may takecertain actions, including asking the user to tighten the watch band,ignoring certain portions of collected heart-rate data (e.g., sectionsof data that have a low quality metric), and weighting certain portionsof the heart-rate data (e.g., data with a higher quality metric may beweighted more heavily when the heart rate is being calculated).

In one embodiment, the signal quality metric may be derived as follows:make a scatter plot where the x-axis is time, and the y-axis is thefrequency of a peak in the PPG signal at that given instant in time. Anissue to be overcome using this strategy is that there may be multipleand/or zero peaks at a given instant in time. A best fit line capturesthe linear relationship in this scatter plot. A high quality signalshould have a set of peaks that fit well to a line (in a short timespan), whereas a bad signal will have a set of peaks that are not welldescribed by a line. Therefore, the quality of the fit to the lineprovides a good metric for the quality of the PPG signal itself.

Sedentary, Sleep, and Active Classified Metrics

In yet another example embodiment, the biometric monitoring device mayemploy sensors to calculate heart rate variability when the devicedetermines the user to be sedentary or asleep. Here, the biometricmonitoring device may operate the sensors in a higher-rate sampling mode(relative to non-sedentary periods or periods of user activity thatexceed a predetermined threshold) to calculate heart rate variability.The biometric monitoring device (or an external device) may employ heartrate variability as an indicator of cardiac health or stress.

Indeed, in some embodiments, the biometric monitoring device may measureand/or determine the user's stress level and/or cardiac health when theuser is sedentary and/or asleep (for example, as detected and/ordetermined by the biometric monitoring device). Some embodiments of abiometric monitoring device of the present disclosure may determine theuser's stress level, health state (e.g., risk, onset, or progression offever or cold), and/or cardiac health using sensor data that isindicative of the heart rate variability, galvanic skin response, skintemperature, body temperature, and/or heart rate. In this way,processing circuitry of the biometric monitoring device may determineand/or track the user's “baseline” stress levels over time and/orcardiac “health” over time. In another embodiment, the device maymeasure a physiologic parameter of the user during one or more periodswhere the user is motionless (or the user's motion is below apredetermined threshold), such as when the user is sitting, lying down,asleep, or in a sleep stage (e.g., deep sleep). Such data may also beemployed by the biometric monitoring device as a “baseline” forstress-related parameters, health-related parameters (e.g., risk oronset of fever or cold), cardiac health, heart rate variability,galvanic skin response, skin temperature, body temperature and/or heartrate.

Sleep Monitoring

In some embodiments, the biometric monitoring device may automaticallydetect or determine when the user is attempting to go to sleep, isentering sleep, is asleep, and/or is awoken from a period of sleep. Insuch embodiments, the biometric monitoring device may employphysiological sensors to acquire data and the data processing circuitryof the biometric monitoring device may correlate a combination of heartrate, heart rate variability, respiration rate, galvanic skin response,motion, skin temperature, and/or body temperature data collected fromsensors of the biometric monitoring device to detect or determine if theuser is attempting to go to sleep, is entering sleep, is asleep, and/oris awoken from a period of sleep. In response, the biometric monitoringdevice may, for example, acquire physiological data (of the types, andin the manners, as described herein) and/or determine physiologicalconditions of the user (of the types, and in the manners, as describedherein). For example, a decrease or cessation of user motion combinedwith a reduction in user heart rate and/or a change in heart ratevariability may indicate that the user has fallen asleep. Subsequentchanges in heart rate variability and galvanic skin response may then beused by the biometric monitoring device to determine transitions of theuser's sleep state between two or more stages of sleep (for example,into lighter and/or deeper stages of sleep). Motion by the user and/oran elevated heart rate and/or a change in heart rate variability may beused by the biometric monitoring device to determine that the user hasawoken.

Real-time, windowed, or batch processing to may be used to determine thetransitions between wake, sleep, and sleep stages. For instance, adecrease in heart rate may be measured in a time window where the heartrate is elevated at the start of the window and reduced in the middle(and/or end) of the window. The awake and sleep stages may be classifiedby a hidden Markov model using changes in motion signal (e.g.,decreasing motion intensity), heart rate, heart rate variability, skintemperature, galvanic skin response, and/or ambient light levels. Thetransition points may be determined through a changepoint algorithm(e.g., Bayesian changepoint analysis). The transition between awake andsleep may be determined by observing periods where the user's heart ratedecreases over a predetermined time duration by at least a certainthreshold but within a predetermined margin of the user's resting heartrate (that is observed as, for example, the minimum heart rate of theuser while sleeping). Similarly, the transition between sleep and awakemay be determined by observing an increase in the user's heart rateabove a predetermined threshold of the user's resting heart rate.

In some embodiments, the biometric monitoring device may be onecomponent of a system for monitoring sleep, where the system includes asecondary device configured to communicate with the biometric monitoringdevice and adapted to be placed near the sleeper (e.g., an alarm clock).The secondary device may, in some implementations, have a shape andmechanical and/or magnetic interface to accept the biometric monitoringdevice for safe keeping, communication, and/or charging. However, thesecondary device may also be generic to the biometric monitoring device,e.g., a smartphone that is not specifically designed to physicallyinterface with the biometric monitoring device. The communicationbetween the biometric monitoring device and the secondary device may beprovided through wired communication interfaces or through wirelesscommunication interfaces and protocols such as Bluetooth (including, forexample, Bluetooth 4.0 and Bluetooth Low Energy protocols), RFID, NFC,or WLAN. The secondary device may include sensors to assist in sleepmonitoring or environmental monitoring such as, for example, sensorsthat measure ambient light, noise and/or sound (e.g., to detectsnoring), temperature, humidity, and air quality (pollen, dust, CO2,etc.). In one embodiment, the secondary device may communicate with anexternal service such as www.fitbit.com or a server (e.g., a personalcomputer). Communication with the secondary device may be achievedthrough wired (e.g., Ethernet, USB) or wireless (e.g., WLAN, Bluetooth,RFID, NFC, cellular) circuitry and protocols to transfer data to and/orfrom the secondary device. The secondary device may also act as a relayto transfer data to and/or from the biometric monitoring device toand/or from an external service such as www.fitbit.com or other service(e.g., data such as news, social network updates, email, calendarnotifications) or server (e.g., personal computer, mobile phone,tablet). Calculation of the user's sleep data may be performed on one orboth devices or an external service (e.g., a cloud server) using datafrom one or both devices.

The secondary device may be equipped with a display to display dataobtained by the secondary device or data transferred to it by thebiometric monitoring device, the external service, or a combination ofdata from the biometric monitoring device, the secondary device, and/orthe external service. For example, the secondary device may display dataindicative of the user's heart rate, total steps for the day, activityand/or sleep goal achievement, the day's weather (measured by thesecondary device or reported for a location by an external service),etc. In another example, the secondary device may display data relatedto the ranking of the user relative to other users, such as total weeklystep count. In yet another embodiment, the biometric monitoring devicemay be equipped with a display to display data obtained by the biometricmonitoring device, the secondary device, the external service, or acombination of the three sources. In embodiments where the first deviceis equipped with a wakeup alarm (e.g., vibramotor, speaker), thesecondary device may act as a backup alarm (e.g., using an audiospeaker). The secondary device may also have an interface (e.g., displayand buttons or touch screen) to create, delete, modify, or enable alarmson the first and/or the secondary device.

Sensor-Based Standby Mode

In another embodiment, the biometric monitoring device may automaticallydetect or determine whether it is or is not attached to, disposed on,and/or being worn by a user. In response to detecting or determiningthat the biometric monitoring device is not attached to, disposed on,and/or being worn by a user, the biometric monitoring device (orselected portions thereof) may implement or be placed in a low powermode of operation—for example, the optical heartbeat waveform sensorand/or circuitry may be placed in a lower power or sleep mode. Forexample, in one embodiment, the biometric monitoring device may includeone or more light detectors (photodiodes, phototransistors, etc.). If,at a given light intensity setting (for example, with respect to thelight emitted by a light source that is part of the biometric monitoringdevice), one or more light detectors provides a low return signal, thebiometric monitoring device may interpret the data as indicative of thedevice not being worn. Upon such a determination, the device may reduceits power consumption—for example, by “disabling” or adjusting theoperating conditions of the stress and/or heart rate detection sensorsand/or circuitry in addition to other device circuitry or displays (forexample, by reducing the duty cycle of or disabling the light source(s)and/or detector(s), turning off the device display, and/or disabling orattenuating associated circuitry or portions thereof). In addition, thebiometric monitoring device may periodically determine (e.g., once persecond) if the operating conditions of the stress and/or heart ratedetection sensors and/or associated circuitry should be restored to anormal operating condition (for example, light source(s), detector(s)and/or associated circuitry should return to a normal operating mode forheart rate detection). In another embodiment, the biometric monitoringdevice may restore the operating conditions of the stress and/or heartrate detection sensors and/or associated circuitry upon detection of atriggerable event—for example, upon detecting motion of the device (forexample, based on data from one or more motion sensor(s)) and/ordetecting a user input via the user interface (for example, a tap, bumpor swipe interaction with the biometric monitoring device). In somerelated embodiments, the biometric monitoring device may, for powersaving purposes, reduce its default rate of heart rate measurementcollection to, for instance, one measurement per minute while the useris not highly active and the user may have the option of putting thedevice into a mode of operation to generate measurements on demand or ata faster rate (e.g., once per second), for instance, by pushing abutton.

Optical Sensor(s)

In one embodiment, the optical sensors (sources and/or detectors) may bedisposed on an interior or skin-side of the biometric monitoring device(i.e., a side of the biometric monitoring device that contacts, touches,and/or faces the skin of the user (hereinafter “skin-side”). (See, forexample, FIGS. 2A through 3C). In another embodiment, the opticalsensors may be disposed on one or more sides of the device, includingthe skin-side and one or more sides of the device that face or areexposed to the ambient environment (environmental side). (See, forexample, FIGS. 6A through 7).

FIG. 6A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 6B illustrates an example of a portable biometric monitoring devicehaving a display and wristband. Additionally, optical PPG (e.g., heartrate) detection sensors and/or emitters may be located on the side ofthe biometric monitoring device. In one embodiment, these may be locatedin side-mounted buttons.

FIG. 7 depicts a user pressing the side of a portable biometricmonitoring device to take a heart rate measurement from a side-mountedoptical heart rate detection sensor. The display of the biometricmonitoring device may show whether or not the heart rate has beendetected and/or display the user's heart rate.

Notably, the data from such optical sensors may be representative ofphysiological data and/or environmental data. Indeed, in one embodiment,the optical sensors provide, acquire and/or detect information frommultiple sides of the biometric monitoring device whether or not thesensors are disposed on one or more of the multiple sides. For example,the optical sensors may obtain data related to the ambient lightconditions of the environment.

Where optical sensors are disposed or arranged on the skin-side of thebiometric monitoring device, in operation, a light source in thebiometric monitoring device may emit light upon the skin of the userand, in response, a light detector in the biometric monitoring devicemay sample, acquire, and/or detect corresponding reflected and/oremitted light from the skin (and from inside the body). The one or morelight sources and light detectors may be arranged in an array or patternthat enhances or optimizes the signal-to-noise ratio and/or serves toreduce or minimize power consumption by the light sources and lightdetectors. These optical sensors may sample, acquire and/or detectphysiological data which may then be processed or analyzed (for example,by resident processing circuitry) to obtain data that is representativeof, for example, a user's heart rate, respiration, heart ratevariability, oxygen saturation (SpO₂), blood volume, blood glucose, skinmoisture, and/or skin pigmentation level.

The light source(s) may emit light having one or more wavelengths thatare specific or directed to a type of physiological data to becollected. Similarly, the optical detectors may sample, measure and/ordetect one or more wavelengths that are also specific or directed to atype of physiological data to be collected and/or a physiologicalparameter (of the user) to be assessed or determined. For instance, inone embodiment, a light source emitting light having a wavelength in thegreen spectrum (for example, an LED that emits light having wavelengthscorresponding to the green spectrum) and a photodiode positioned tosample, measure, and/or detect a response or reflection correspondingwith such light may provide data that may be used to determine or detectheart rate. In contrast, a light source emitting light having awavelength in the red spectrum (for example, an LED that emits lighthaving wavelengths corresponding to the red spectrum) and a light sourceemitting light having a wavelength in the infrared spectrum (forexample, an LED that emits light having wavelengths corresponding to theIR spectrum) and photodiode positioned to sample, measure and/or detecta response or reflection of such light may provide data used todetermine or detect SpO₂.

Indeed, in some embodiments, the color or wavelength of the lightemitted by the light source, e.g., an LED (or set of LEDs), may bemodified, adjusted, and/or controlled in accordance with a predeterminedtype of physiological data being acquired or conditions of operation.Here, the wavelength of the light emitted by the light source may beadjusted and/or controlled to optimize and/or enhance the “quality” ofthe physiological data obtained and/or sampled by the detector. Forexample, the color of the light emitted by the LED may be switched frominfrared to green when the user's skin temperature or the ambienttemperature is cool in order to enhance the signal corresponding tocardiac activity. (See, for example, FIG. 11D).

The biometric monitoring device, in some embodiments, may include awindow (for example, a window that is, to casual inspection, opaque) inthe housing to facilitate optical transmission between the opticalsensors and the user. Here, the window may permit light (for example, ofa selected wavelength) to be emitted by, for example, one or more LEDs,onto the skin of the user and a response or reflection of that light topass back through the window to be sampled, measured, and/or detectedby, for example, one or more photodiodes. In one embodiment, thecircuitry related to emitting and receiving light may be disposed in theinterior of the device housing and underneath or behind a plastic orglass layer (for example, painted with infrared ink) or an infrared lensor filter that permits infrared light to pass but not light in the humanvisual spectrum. In this way, the light transmissivity of the window maybe invisible to the human eye.

The biometric monitoring device may employ light pipes or otherlight-transmissive structures to facilitate transmission of light fromthe light sources to the user's body and skin. (See, for example, FIGS.4A through 5). In this regard, in some embodiments, light may bedirected from the light source to the skin of the user through suchlight pipes or other light-transmissive structures. Scattered light fromthe user's body may be directed back to the optical circuitry in thebiometric monitoring device through the same or similar structures.Indeed, the light-transmissive structures may employ a material and/oroptical design to facilitate low light loss (for example, thelight-transmissive structures may include a lens to facilitate lightcollection, and portions of the light-transmissive structures may becoated with or adjacent to reflective materials to promote internalreflection of light within the light-transmissive structures) therebyimproving the signal-to-noise-ratio of the photo detector and/orfacilitating reduced power consumption of the light source(s) and/orlight detectors. In some embodiments, the light pipes or otherlight-transmissive structures may include a material that selectivelytransmits light having one or more specific or predetermined wavelengthswith higher efficiency than others, thereby acting as a bandpass filter.Such a bandpass filter may be tuned to improve the signal of a specificphysiological data type. For example, in one embodiment, anIn-Mold-Labeling or “IML” light-transmissive structure may beimplemented wherein the light-transmissive structure uses a materialwith predetermined or desired optical characteristics to create aspecific bandpass characteristic, for example, so as to pass infraredlight with greater efficiency than light of other wavelengths (forexample, light having a wavelength in human visible spectrum). Inanother embodiment, a biometric monitoring device may employ alight-transmissive structure having an optically opaque portion(including certain optical properties) and an optically-transparentportion (including optical properties different from theoptically-opaque portion). Such a light-transmissive structure may beprovided via a double-shot or two-step molding process wherein opticallyopaque material and optically transparent material are separatelyinjected into a mold. A biometric monitoring device implementing such alight-transmissive structure may include different light transmissivityproperties for different wavelengths depending on the direction of lighttravel through the light-transmissive structure. For example, in oneembodiment, the optically-opaque material may be reflective to aspecific wavelength range so as to more efficiently transport light fromthe user's body back to the light detector (which may be of a differentwavelength(s) relative to the wavelength(s) of the emitted light).

In another embodiment, reflective structures may be placed in the fieldof view of the light emitter(s) and/or light detector(s). For example,the sides of holes that channel light from light emitter(s) to a user'sskin and/or from the user's skin to light detector(s) (or through whichlight-transmissive structures that perform such channeling travel) maybe covered in a reflective material (e.g., chromed) to facilitate lighttransmission. The reflective material may increase the efficiency withwhich the light is transported to the skin from the light source(s) andthen from the skin back into the detector(s). The reflectively-coatedhole may be filled in with an optical epoxy or other transparentmaterial to prevent liquid from entering the device body while stillallowing light to be transmitted with low transmission loss.

In another embodiment that implements light-transmissive structures (forexample, structures created or formed through IML), suchlight-transmissive structures may include a mask consisting of an opaquematerial that limits the aperture of one, some, or all of the lightsource(s) and/or detector(s). In this way, the light-transmissivestructures may selectively “define” a preferential volume of the user'sbody that light is emitted into and/or detected from. Notably, othermask configurations may be employed or implemented in connection withthe concepts described and/or illustrated herein; all such maskingconfigurations to, for example, improve the photoplethysmography signaland which are implemented in connection with the concepts describedand/or illustrated herein are intended to fall within the scope of thepresent disclosure.

In another embodiment, the light emitter(s) and/or detector(s) may beconfigured to transmit light through a hole or series of holes in thedevice exterior. This hole or series of holes may be filled in withlight-transmissive epoxy (e.g. optical epoxy). The epoxy may form alight pipe that allows light to be transmitted from the light emitter(s)to the skin and from the skin back into the light detector(s). Thistechnique also has the advantage that the epoxy may form a watertightseal, preventing water, sweat or other liquid from entering the devicebody though the hole(s) on the device exterior that allow the lightemitter(s) and detector(s) to transmit to, and receive light from, thebiometric monitoring device body exterior. An epoxy with a high thermalconductivity may be used to help prevent the light source(s) (e.g.,LED's) from overheating.

In any of the light-transmissive structures described herein, theexposed surfaces of the optics (light-transmissive structures) or devicebody may include a hard coat paint, hard coat dip, or optical coatings(such as anti-reflection, scratch resistance, anti-fog, and/orwavelength band block (such as ultraviolet light blocking) coatings).Such characteristics or materials may improve the operation, accuracyand/or longevity of the biometric monitoring device.

FIG. 4A illustrates an example of one potential PPG light source andphotodetector geometry. In this embodiment, two light sources are placedon either side of a photodetector. These three devices are located in aprotrusion on the back of a wristband-type biometric monitoring device(the side which faces the skin of the user).

FIGS. 4B and 4C illustrate examples of a PPG sensor having aphotodetector and two LED light sources. These components are placed ina biometric monitoring device that has a protrusion on the back side.Light pipes optically connect the LEDs and photodetector with thesurface of the user's skin. Beneath the skin, the light from the lightsources scatters off of blood in the body, some of which may bescattered or reflected back into the photodetector.

FIG. 5 Illustrates an example of a biometric monitoring device with anoptimized PPG detector that has a protrusion with curved sides so as notto discomfort the user. Additionally, the surface of light pipes thatoptically couple the photodetector and the LEDs to the wearer's skin arecontoured to maximize light flux coupling between the LEDs andphotodetectors and the light pipes. The ends of the light pipes thatface the user's skin are also contoured. This contour may focus ordefocus light to optimize the PPG signal. For example, the contour mayfocus emitted light to a certain depth and location that coincides withan area where blood flow is likely to occur. The vertex of these focimay overlap or be very close together so that the photodetector receivesthe maximum possible amount of scattered light.

In some embodiments, the biometric monitoring device may include aconcave or convex shape, e.g., a lens, on the skin-side of the device,to focus light towards a specific volume at a specific depth in the skinand increase the efficiency of light collected from that point into thephotodetector. (See, for example, FIGS. 4A through 5). Where such abiometric monitoring device also employs light pipes to selectively andcontrollably route light, it may be advantageous to shape the end of thelight pipe with a degree of cylindricity, e.g., the end of the lightpipe may be a be a cylindrical surface (or portion thereof) defined by acylinder axis that is nominally parallel to the skin-side (for example,rather than use an axially-symmetric lens). For example, in awristband-style biometric monitoring device, such a cylindrical lens maybe oriented such that the cylinder axis is nominally parallel to thewearer's forearm, which may have the effect of limiting the amount oflight that enters such a lens from directions parallel to the person'sforearm and increasing the amount of light that enters such a lens fromdirections perpendicular to the person's forearm—since ambient light ismore likely to reach the sensor detection area from directions that arenot occluded by the straps of the biometric monitoring device, i.e.,along the user's forearm axis, than from directions that are occluded bythe straps, i.e., perpendicular to the user's forearm. Such aconfiguration may improve the signal-to-noise-ratio by increasing theefficiency of light transferred from the emitter onto or into the skinof the user while decreasing “stray” light from being detected orcollected by the photodetector. In this way, the signal sampled,measured and/or detected by the photodetector consists less of straylight and more of the user's skin/body response to such emitted light(signal or data that is representative of the response to the emittedlight).

In another embodiment, light-transmissive epoxy may be molded into aconcave or convex shape so as to provide beneficial optical propertiesto sensors as well. For example, during the application of lighttransmissive epoxy, the top of the light-transmissive structure that isformed by the epoxy may be shaped into a concave surface so that lightcouples more effectively into the light-transmissive structure.

In one embodiment, the components of the optical sensor may bepositioned on the skin-side of the device and arranged or positioned toreduce or minimize the distance between (i) the light source(s) and/orthe associated detector(s) and (ii) the skin of the user. See, forexample, FIG. 3A, which provides a cross-sectional view of a sensorprotrusion of an example portable monitoring device. In FIG. 3A, twolight sources (e.g., LEDs) are placed on either side of a photodetectorto enable PPG sensing. A light-blocking material is placed between thelight sources and the photodetector to prevent any light from the lightsources from reaching photodetector without first exiting the body ofthe biometric monitoring device. A flexible transparent layer may beplaced on the lower surface of the sensor protrusion to form a seal.This transparent layer may serve other functions such as preventingliquid from entering the device where the light sources orphotodetectors are placed. This transparent layer may be formed throughin-mold labeling or “IML”. The light sources and photodetector may beplaced on a flexible PCB.

Such a configuration may improve the efficiency of light flux couplingbetween the components of the optical sensor and the user's body. Forexample, in one embodiment, the light source(s) and/or associateddetector(s) may be disposed on a flexible or pliable substrate that mayflex, allowing the skin-side of the biometric monitoring device, whichmay be made from a compliant material, to conform (for example, withoutadditional processing) or be capable of being shaped (or compliant) toconform to the shape of the body part (for example, the user's wrist,arm, ankle, and/or leg) to which the biometric monitoring device iscoupled to or attached during normal operation so that the lightsource(s) and/or associated detector(s) are/is close to the skin of theuser (i.e., with little to no gap between the skin-side of the deviceand the adjacent surface of the skin of the user. See, for example, FIG.6A. In one embodiment, the light source(s) and/or associated detector(s)may be disposed on a Flat Flex Cable or “FFC” or flexible PCB. In thisembodiment, the flexible or pliable substrate (for example, an FFC orflexible PCB) may connect to a second substrate (for example, PCB)within the device having other components disposed thereon (for example,the data processing circuitry). Optical components of differing heightsmay be mounted to different “fingers” of flexible substrate and pressedor secured to the housing surface such that the optical components areflush to the housing surface. In one embodiment, the second substratemay be a relatively inflexible or non-pliable substrate, fixed withinthe device, having other circuitry and components (passive and/oractive) disposed thereon.

FIG. 3B depicts a cross-sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 3A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 3C provides another cross-sectional view of an example PPG sensorimplementation. Of note in this PPG sensor is the lack of a protrusion.Additionally, a liquid gasket and/or a pressure sensitive adhesive areused to prevent liquid from entering the biometric monitoring devicebody.

Some embodiments of biometric monitoring devices may be adapted to beworn or carried on the body of a user. In some embodiments including theoptical heart rate monitor, the device may be a wrist-worn orarm-mounted accessory such as a watch or bracelet. (See, for example,FIGS. 2A through 7). In one embodiment, optical elements of the opticalheart rate monitor may be located on the interior or skin-side of thebiometric monitoring device, for example, facing the top of the wrist(i.e., the optical heart rate monitor may be adjacent to and facing thewrist) when the biometric monitoring device is worn on the wrist. (See,for example, FIGS. 2A through 3C).

In another embodiment, the optical heart rate monitor may be located onone or more external or environmental side surfaces of the biometricmonitoring device. (See, for example, FIGS. 6B and 7). In suchembodiments, the user may touch an optical window (behind which opticalelements of the optical heart rate monitor are located) with a finger onthe opposing hand to initiate a heart rate measurement (and/or othermetrics related to heart rate such as heart rate variability) and/orcollect data which may be used to determine the user's heart rate(and/or other metrics related to heart rate). (See, for example, FIG.6B). In one embodiment, the biometric monitoring device may trigger orinitiate the measurement(s) by detecting a (sudden) drop in incidentlight on the photodiode—for example, when the user's finger is placedover the optical window. In addition thereto, or in lieu thereof, aheart rate measurement (or other such metric) may be trigged by anIR-based proximity detector and/or capacitive touch/proximity detector(which may be separate from other detectors). Such IR-based proximitydetector and/or capacitive touch/proximity detector may be disposed inor on and/or functionally, electrically and/or physically coupled to theoptical window to detect or determine the presence of, for example, theuser's finger.

In yet another embodiment, the biometric monitoring device may include abutton that, when depressed, triggers or initiates heart ratemeasurement (and/or other metrics related to heart rate). The button maybe disposed in close proximity to the optical window to facilitate theuser pressing the button while the finger is disposed on the opticalwindow. (See, for example, FIG. 7). In one embodiment, the opticalwindow may be embedded in a push button. Thus, when the user presses thebutton, it may trigger a measurement of the finger that depresses thebutton. Indeed, the button may be given a shape and/or resistance topressing that enhances or optimizes a pressure profile of the buttonagainst the finger to provide a high signal-to-noise-ratio duringmeasurement or data acquisition. In other embodiments (not illustrated),the biometric monitoring device may take the form of a clip, a smoothobject, a pendant, an anklet, a belt, etc. that is adapted to be worn onthe body, clipped or mounted to an article of clothing, deposited inclothing (e.g., in a pocket), or deposited in an accessory (e.g.,handbag).

In one specific embodiment, the biometric monitoring device may includea protrusion on the skin- or interior side of the device. (See, FIGS. 2Athrough 6A). When coupled to the user, the protrusion may engage theskin with more force than the surrounding device body. In thisembodiment, an optical window or light transmissive structure (both ofwhich are discussed in detail above) may form or be incorporated in aportion of the protrusion. The light emitter(s) and/or detector(s) ofthe optical sensor may be disposed or arranged in the protrusion nearthe window or light transmissive structure. (See, for example, FIGS. 2Band 6A). As such, when attached to the user's body, the window portionof the protrusion of the biometric monitoring device may engage theuser's skin with more force than the surrounding device body—therebyproviding a more secure physical coupling between the user's skin andthe optical window. That is, the protrusion may cause sustained contactbetween the biometric monitoring device and the user's skin that mayreduce the amount of stray light measured by the photodetector, decreaserelative motion between the biometric monitoring device and the user,and/or provide improved local pressure to the user's skin; all of whichmay increase the quality of the cardiac signal of interest. Notably, theprotrusion may contain other sensors that benefit from close proximityand/or secure contact to the user's skin. These may be included inaddition to or in lieu of a heartbeat waveform sensor and includesensors such as a skin temperature sensor (e.g., noncontact thermopilethat utilizes the optical window or thermistor joined with thermal epoxyto the outer surface of the protrusion), pulse oximeter, blood pressuresensor, EMG, or galvanic skin response (GSR) sensor.

In addition thereto, or in lieu thereof, a portion of the skin-side ofthe biometric monitoring device may include a friction enhancingmechanism or material. For example, the skin-side of the biometricmonitoring device may include a plurality of raised or depressed regionsor portions (for example, small bumps, ridges, grooves, and/or divots).Moreover, a friction enhancing material (for example, a gel-likematerial such as silicone or other elastomeric material) may be disposedon the skin-side. Indeed, a device back made out of gel may also providefriction while also improving user comfort and preventing stray lightfrom entering. As noted above, a friction-enhancing mechanism ormaterial may be used alone or in conjunction with the biometricmonitoring device having a protrusion as described herein. In thisregard, the biometric monitoring device may include a plurality ofraised or depressed regions portions (for example, small bumps, ridges,grooves, and/or divots) in or on the protrusion portion of the device.Indeed, such raised or depressed regions portions may beincorporated/embedded into or on a window portion of the protrusion. Inaddition thereto, or in lieu thereof, the protrusion portion may consistof or be coated with a friction enhancing material (for example, agel-like material such as silicone). Notably, the use of a protrusionand/or friction may improve measurement accuracy of data acquisitioncorresponding to certain parameters (e.g., heart rate, heart ratevariability, galvanic skin response, skin temperature, skin coloration,heat flux, blood pressure, blood glucose, etc.) by reducing motion ofthe biometric monitoring device (and thus of the sensor) relative to theuser's skin during operation, especially while the user is in motion.

Some or all of the interior or skin-side housing of the biometricmonitoring device may also consist of a metal material (for example,steel, stainless steel, aluminum, magnesium, or titanium). Such aconfiguration may provide a structural rigidity. (See, for example, FIG.2B). In such an embodiment, the device body may be designed to behypoallergenic through the use of a hypoallergenic “nickel-free”stainless steel. Notably, it may be advantageous to employ (at least incertain locations) a type of metal that is at least somewhat ferrous(for example, a grade of stainless steel that is ferrous). In suchembodiments, the biometric monitoring device (where it includes arechargeable energy source (for example, rechargeable battery)) mayinterconnect with a charger via a connector that secures itself to thebiometric monitoring device using magnets that couple to the ferrousmaterial. In addition, biometric monitoring device may also engage adock or dock station, using such magnetic properties, to facilitate datatransfer. Moreover, such a housing may provide enhanced electromagneticshielding that would enhance the integrity and reliability of theoptical heartbeat waveform sensor and the heart rate data acquisitionprocess/operation. Furthermore, a skin temperature sensor may bephysically and thermally coupled, for example, with thermal epoxy, tothe metal body to sense the temperature of the user. In embodimentsincluding a protrusion, the sensor may be positioned near or in theprotrusion to provide secure contact and localized thermal coupling tothe user's skin.

In a preferred embodiment, one or more components of the optical sensor(which may, in one embodiment, be located in a protrusion, and/or inanother embodiment, may be disposed or placed flush to the surface ofthe biometric monitoring device) are attached, fixed, included, and/orsecured to the biometric monitoring device via a liquid-tight seal(i.e., a method/mechanism that prevents liquid ingress into the body ofthe biometric monitoring device). For example, in one embodiment, adevice back made out of a metal such as, but not limited to, stainlesssteel, aluminum, magnesium, or titanium, or from a rigid plastic mayprovide a structure that is stiff enough to maintain the structuralintegrity of the device while accommodating a watertight seal for thesensor package. (See, for example, FIGS. 2B through 3C).

In a preferred embodiment, a package or module of the optical sensor maybe connected to the device with a pressure-sensitive adhesive and aliquid gasket. See, for example, FIG. 3C, which provides anothercross-sectional view of a PPG sensor implementation. Of note in this PPGsensor is the lack of a protrusion. Additionally, a liquid gasket and/ora pressure sensitive adhesive are used to prevent liquid from enteringthe device body. Screws, rivets or the like may also be used, forexample, if a stronger or more durable connection is required betweenthe optical sensor package/module and the device body. Notably, thepresent embodiments may also use watertight glues, hydrophobic membranessuch as Gore-Tex, o-rings, sealant, grease, or epoxy to secure or attachthe optical sensor package/module to the biometric monitoring devicebody.

As discussed above, the biometric monitoring device may include amaterial disposed on the skin- or interior side that includes highreflectivity characteristics—for example, polished stainless steel,reflective paint, and polished plastic. In this way, light scattered offthe skin-side of the device may be reflected back into the skin in orderto, for example, improve the signal-to-noise-ratio of an opticalheartbeat waveform sensor. Indeed, this effectively increases the inputlight signal as compared with a device body back that is non-reflective(or less reflective). Notably, in one embodiment, the color of the skinor interior side of the biometric monitoring device may be selected toprovide certain optical characteristics (for example, reflect certain orpredetermined wavelengths of light), in order to improve the signal withrespect to certain physiological data types. For example, where theskin- or interior side of the biometric monitoring is green, themeasurements of the heart rate may be enhanced due to the preferentialemission of a wavelength of the light corresponding to the greenspectrum. Where the skin- or interior side of the biometric monitoringis red, the measurements of the SpO₂ may be enhanced due to the emissionpreferential of a wavelength of the light corresponding to the redspectrum. In one embodiment, the color of the skin- or interior side ofthe biometric monitoring may be modified, adjusted and/or controlled inaccordance with a predetermined type of physiological data beingacquired.

FIG. 11A depicts an example schematic block diagram of an opticalheartbeat waveform sensor where light is emitted from a light sourcetoward the user's skin and the reflection of such light from theskin/internal body of the user is sensed by a light detector, the signalfrom which is subsequently digitized by an analog to digital converter(ADC). The intensity of the light source may be modified (e.g., througha light source intensity control module) to maintain a desirablereflected signal intensity. For example, the light source intensity maybe reduced to avoid saturation of the output signal from the lightdetector. As another example, the light source intensity may beincreased to maintain the output signal from the light detector within adesired range of output values. Notably, active control of the systemmay be achieved through linear or nonlinear control methods such asproportional-integral-derivative (PID) control, fixed step control,predictive control, neural networks, hysteresis, and the like, and mayalso employ information derived from other sensors in the device such asmotion, galvanic skin response, etc. FIG. 11A is provided forillustration and does not limit the implementation of such a system to,for instance, an ADC integrated within a MCU, or the use of a MCU forthat matter. Other possible implementations include the use of one ormore internal or external ADCs, FPGAs, ASICs, etc.

In another embodiment, system with an optical heartbeat waveform sensormay incorporate the use of a sample-and-hold circuit (or equivalent) tomaintain the output of the light detector while the light source isturned off or attenuated to save power. In embodiments where relativechanges in the light detector output are of primary importance (e.g.,heart rate measurement), the sample-and-hold circuit may not have tomaintain an accurate copy of the output of the light detector. In suchcases, the sample-and-hold may be reduced to, for example, a diode(e.g., Schottky diode) and capacitor. The output of the sample-and-holdcircuit may be presented to an analog signal conditioning circuit (e.g.,a Sallen-Key bandpass filter, level shifter, and/or gain circuit) tocondition and amplify the signal within frequency bands of interest(e.g., 0.1 Hz to 10 Hz for cardiac or respiratory function), which maythen be digitized by the ADC. See, for example, FIG. 11B.

In operation, circuit topologies such as those already described herein(e.g. a sample-and-hold circuit) remove the DC and low frequencycomponents of the signal and help resolve the AC component related toheart rate and/or respiration. The embodiment may also include theanalog signal conditioning circuitry for variable gain settings that canbe controlled to provide a suitable signal (e.g., not saturated). Theperformance characteristics (e.g., slew rate and/or gain bandwidthproduct) and power consumption of the light source, light detector,and/or sample-and-hold may be significantly higher than the analogsignal conditioning circuit to enable fast duty cycling of the lightsource. In some embodiments, the power provided to the light source andlight detector may be controlled separately from the power provided tothe analog signal conditioning circuit to provide additional powersavings. Alternatively or additionally, the circuitry can usefunctionality such as an enable, disable and/or shutdown to achievepower savings. In another embodiment, the output of the light detectorand/or sample-and-hold circuit may be sampled by an ADC in addition toor in lieu of the analog signal conditioning circuit to control thelight intensity of the light source or to measure the physiologicparameters of interest when, for example, the analog signal conditioningcircuit is not yet stable after a change to the light intensity setting.Notably, because the physiologic signal of interest is typically smallrelative to the inherent resolution of the ADC, in some embodiments, thereference voltages and/or gain of the ADC may be adjusted to enhancesignal quality and/or the ADC may be oversampled. In yet anotherembodiment, the device may digitize the output of only thesample-and-hold circuit by, for example, oversampling, adjusting thereference voltages and/or gain of the ADC, or using a high resolutionADC. See, for example, FIG. 11C.

PPG DC Offset Removal Techniques

In another embodiment, the sensor device may incorporate a differentialamplifier to amplify the relative changes in the output of the lightdetector. See, for example, FIG. 11F. In some embodiments, a digitalaverage or digital low-pass filtered signal may be subtracted from theoutput of the light detector. This modified signal may then be amplifiedbefore it is digitized by the ADC. In another embodiment, an analogaverage or analog low-pass filtered signal may be subtracted from theoutput of the light detector through, for example, the use of asample-and-hold circuit and analog signal conditioning circuitry. Thepower provided to the light source, light detector, and differentialamplifier may be controlled separately from the power provided to theanalog signal conditioning circuit to improve power savings.

In another embodiment, a signal (voltage or current, depending on thespecific sensor implementation) may be subtracted from the raw PPGsignal to remove any bias in the raw PPG signal and therefore increasethe gain or amplification of the PPG signal that contains heart rate (orother circulatory parameters such as heart rate variability)information. This signal may be set to a default value in the factory,to a value based on the user's specific skin reflectivity, absorption,and/or color, and/or may change depending on feedback from an ambientlight sensor, or depending on analytics of the PPG signal itself. Forexample, if the PPG signal is determined to have a large DC offset, aconstant voltage may be subtracted from the PPG signal to remove the DCoffset and enable a larger gain, therefore improving the PPG signalquality. The DC offset in this example may result from ambient light(for example from the sun or from indoor lighting) reaching thephotodetector from or reflected light from the PPG light source.

In another embodiment, a differential amplifier may be used to measurethe difference between current and previous samples rather than themagnitude of each signal. Since the magnitude of each sample istypically much greater than the difference between each sample, a largergain can be applied to each measurement, therefore improving the PPGsignal quality. The signal may then be integrated to obtain the originaltime domain signal.

In another embodiment, the light detector module may incorporate atransimpedance amplifier stage with variable gain. Such a configurationmay avoid or minimize saturation from bright ambient light and/or brightemitted light from the light source. For example, the gain of thetransimpedance amplifier may be automatically reduced with a variableresistor and/or multiplexed set of resistors in the negative feedbackpath of the transimpedance amplifier. In some embodiments, the devicemay incorporate little to no optical shielding from ambient light byamplitude-modulating the intensity of the light source and thendemodulating the output of the light detector (e.g., synchronousdetection). See, for instance, FIG. 11E. In other aspects, if theambient light is of sufficient brightness to obtain a heart rate signal,the light source may be reduced in brightness and/or turned offcompletely.

In yet another embodiment, the aforementioned processing techniques maybe used in combination to optically measure physiological parameters ofthe user. See, for example, FIG. 11G. This topology may allow the systemto operate in a low power measurement state and circuit topology whenapplicable and adapt to a higher power measurement state and circuittopology as necessary. For instance, the system may measure thephysiologic parameter (e.g., heart rate) of interest using analogsignal-conditioning circuitry while the user is immobile or sedentary toreduce power consumption, but switch to oversampled sampling of thelight detector output directly while the user is active.

In embodiments where the biometric monitoring device includes a heartrate monitor, processing of the signal to obtain heart rate measurementsmay include filtering and/or signal conditioning such as band-passfiltering (e.g., Butterworth filter). To counteract large transientsthat may occur in the signal and/or to improve convergence of saidfiltering, nonlinear approaches may be employed such as neural networksor slew rate limiting. Data from the sensors on the device such asmotion, galvanic skin response, skin temperature, etc., may be used toadjust the signal conditioning methods employed. Under certain operatingconditions, the heart rate of the user may be measured by counting thenumber of signal peaks within a time window or by utilizing thefundamental frequency or second harmonic of the signal (e.g., through afast Fourier transform (FFT)). In other cases, such as heart rate dataacquired while the user is in motion, FFTs may be performed on thesignal and spectral peaks extracted, which may then be subsequentlyprocessed by a multiple-target tracker which starts, continues, merges,and deletes tracks of the spectra. In some embodiments, a similar set ofoperations may be performed on the motion signal and the output may beused to do activity discrimination (e.g., sedentary, walking, running,sleeping, lying down, sitting, biking, typing, elliptical, weighttraining) which is used to assist the multiple-target tracker. Forinstance, it may be determined that the user was stationary and hasbegun to move. This information may be used to preferentially bias thetrack continuation toward increasing frequencies. Similarly, theactivity discriminator may determine that the user has stopped runningor is running slower and this information may be used to preferentiallybias the track continuation toward decreasing frequencies. Tracking maybe achieved with single-scan or multi-scan, multiple-target trackertopologies such as joint probabilistic data association trackers,multiple-hypothesis tracking, nearest neighbor, etc. Estimation andprediction in the tracker may be done through Kalman filters, splineregression, particle filters, interacting multiple model filters, etc. Atrack selector module may use the output tracks from themultiple-spectra tracker and estimate the user's heart rate. Theestimate may be taken as the maximum likelihood track, a weight sum ofthe tracks against their probabilities of being the heart rate, etc. Theactivity discriminator may furthermore influence the selection and/orfusion to get the heart rate estimate. For instance, if the user issleeping, sitting, lying down, or sedentary, a prior probability may beskewed toward heart rates in the 40-80 bpm range; whereas if the user isrunning, jogging, or doing other vigorous exercise, a prior probabilitymay be skewed toward elevated heart rates in the 90-180 bpm range. Theinfluence of the activity discriminator may be based on the speed of theuser. The estimate may be shifted toward (or wholly obtained by) thefundamental frequency of the signal when the user is not moving. Thetrack that corresponds to the user's heart rate may be selected based oncriteria that are indicative of changes in activity; for instance, ifthe user begins to walk from being stationary, the track thatillustrates a shift toward higher frequency may be preferentiallychosen.

The acquisition of a good heart rate signal may be indicated to the userthrough a display on the biometric monitoring device or another devicein wired or wireless communication with the biometric monitoring device(e.g., a Bluetooth Low Energy-equipped mobile phone). In someembodiments, the biometric monitoring device may include asignal-strength indicator that is represented by the pulsing of an LEDviewable by the user. The pulsing may be timed or correlated to becoincident with the user's heartbeat. The intensity, pulsing rate and/orcolor of the LED may be modified or adjusted to suggest signal strength.For example, a brighter LED intensity may represent a stronger signal orin an RGB LED configuration, a green colored LED may represent astronger signal.

In some embodiments, the strength of the heart rate signal may bedetermined by the energy (e.g., squared sum) of the signal in afrequency band of, for instance, 0.5 Hz to 4 Hz. In other embodiments,the biometric monitoring device may have a strain gauge, pressuresensor, force sensor, or other contact-indicating sensor that may beincorporated or constructed into the housing and/or in the band (inthose embodiments where the biometric monitoring device is attached toor mounted with a band like a watch, bracelet, and/or armband—which maythen be secured to the user). A signal quality metric (e.g. heart ratesignal quality) may be calculated based on data from these contactsensors either alone or in combination with data from the heart ratesignal.

In another embodiment, the biometric monitoring device may monitor heartrate optically through an array of photodetectors such as a grid ofphotodiodes or a CCD camera. Motion of the optical device with respectto the skin may be tracked through feature-tracking of the skin and/oradaptive motion correction using an accelerometer and gyroscope. Thedetector array may be in contact with the skin or offset at a smalldistance away from the skin. The detector array and its associatedoptics may be actively controlled (e.g., with a motor) to maintain astabilized image of the target and acquire a heart rate signal. Thisoptomechanical stabilization may be achieved using information frommotion sensors (e.g., a gyroscope) or image features. In one embodiment,the biometric monitoring device may implement relative motioncancellation using a coherent or incoherent light source to illuminatethe skin and a photodetector array with each photodetector associatedwith comparators for comparing the intensity between neighboringdetectors—obtaining a so-called speckle pattern which may be trackedusing a variety of image tracking techniques such as optical flow,template matching, edge tracking, etc. In this embodiment, the lightsource used for motion tracking may be different than the light sourceused in the optical heart rate monitor.

In another embodiment, the biometric monitoring device may consist of aplurality of photodetectors and photoemitters distributed along asurface of the device that touches the user's skin (i.e., the skin-sideof the biometric monitoring device). (See, for example, FIGS. 2A through6A). In the example of a bracelet, for instance, there may be aplurality of photodetectors and photoemitters placed at various sitesalong the circumference of the interior of the band. (See, for example,FIG. 6A). A heart rate signal-quality metric associated with each sitemay be calculated to determine the best or set of best sites forestimating the user's heart rate. Subsequently, some of the sites may bedisabled or turned off to, for example, reduce power consumption. Thedevice may periodically check the heart rate signal quality at some orall of the sites to enhance, monitor and/or optimize signal and/or powerefficiency.

In another embodiment, a biometric monitoring device may include a heartrate monitoring system including a plurality of sensors such as optical,acoustic, pressure, electrical (e.g., ECG or EKG), and motion and fusethe information from two or more of these sensors to provide an estimateof heart rate and/or mitigate noise induced from motion.

In addition to heart rate monitoring (or other biometric monitoring), orin lieu thereof, the biometric monitoring device, in some embodiments,may include optical sensors to track or detect time and duration ofultraviolet light exposure, total outdoor light exposure, the type oflight source and duration and intensity of that light source(fluorescent light exposure, incandescent bulb light exposure, halogen,etc.), exposure to television (based on light type and flicker rate),whether the user is indoors or outdoors, time of day and location basedon light conditions. In one embodiment, the ultraviolet detection sensormay consist of a reverse biased LED emitter driven as a light detector.The photocurrent produced by this detector may be characterized by, forinstance, measuring the time it takes for the LED's capacitance (oralternately a parallel capacitor) to discharge.

All of the optical sensors discussed herein may be used in conjunctionwith other sensors to improve detection of the data described above orbe used to augment detection of other types of physiological orenvironmental data.

Where the biometric monitoring device includes an audio or passiveacoustic sensor, the device may contain one or more passive acousticsensors that detect sound and pressure and that can include, but are notlimited to, microphones, piezo films, etc. The acoustic sensors may bedisposed on one or more sides of the device, including the side thattouches or faces the skin (skin-side) and the sides that face theenvironment (environmental sides).

Skin-side acoustic or audio sensors may detect any type of soundtransmitted through the body and such sensors may be arranged in anarray or pattern that optimizes both the signal-to-noise-ratio and powerconsumption of such sensors. These sensors may detect respiration (e.g.,by listening to the lung), respiratory sounds (e.g., breathing, snoring)and problems (e.g., sleep apnea, etc.), heart rate (listening to theheart beat), user's voice (via sound transmitted from the vocal cordsthroughout the body).

The biometric monitoring devices of the present disclosure may alsoinclude galvanic skin-response (GSR) circuitry to measure the responseof the user's skin to emotional and physical stimuli or physiologicalchanges (e.g., the transition of sleep stage). In some embodiments, thebiometric monitoring device may be a wrist- or arm-mounted deviceincorporating a band made of conductive rubber or fabric so that thegalvanic skin response electrodes may be hidden in the band. Because thegalvanic skin response circuitry may be subjected to changingtemperatures and environmental conditions, it may also include circuitryto enable automatic calibration, such as two or more switchablereference resistors in parallel or in series with the humanskin/electrode path that allows real-time measurement of known resistorsto characterize the response of the galvanic skin response circuit. Thereference resistors may be switched into and out of the measurement pathsuch that they are measured independently and/or simultaneously with theresistance of the human skin.

Circuits for Performing PPG

PPG circuitry may be optimized to obtain the best quality signalregardless of a variety of environmental conditions including, but notlimited to, motion, ambient light, and skin color. The followingcircuits and techniques may be used to perform such optimization (seeFIGS. 16A through 16J);

a sample-and-hold circuit and differential/instrumentation amplifierwhich may be used in PPG sensing. The output signal is an amplifieddifference between current and previous sample, referenced to a givenvoltage.

controlled current source to offset “bias” current prior totransimpedance amplifier. This allows greater gain to be applied attransimpedance amplifier stage.

a sample-and-hold circuit for current feedback applied to photodiode(prior to transimpedance amplifier). This can be used for ambient lightremoval, or “bias” current removal, or as a pseudo differentialamplifier (may require dual rails).

a differential/instrumentation amplifier with ambient lightcancellation.

a photodiode offset current generated dynamically by a DAC.

a photodiode offset current generated dynamically by controlled voltagesource.

ambient light removal using a “switched capacitor” method.

photodiode offset current generated by a constant current source (alsocan be done with a constant voltage source and a resistor).

ambient light removal and differencing between consecutive samples.

FIG. 16A illustrates an example schematic of a sample-and-hold circuitand differential/instrumentation amplifier which may be used in PPGsensing. The output signal in such a circuit may be an amplifieddifference between a current sample and a previous sample, referenced toa given voltage.

FIG. 16B illustrates an example schematic of a circuit for a PPG sensorusing a controlled current source to offset “bias” current prior to atransimpedance amplifier. This allows greater gain to be applied at thetransimpedance amplifier stage.

FIG. 16C illustrates an example schematic of a circuit for a PPG sensorusing a sample-and-hold circuit for current feedback applied tophotodiode (prior to a transimpedance amplifier). This circuit may beused for ambient light removal, or “bias” current removal, or as apseudo-differential amplifier.

FIG. 16D illustrates an example schematic of a circuit for a PPG sensorusing a differential/instrumentation amplifier with ambient lightcancellation functionality.

FIG. 16E illustrates an example schematic of a circuit for a PPG sensorusing a photodiode offset current generated dynamically by a DAC.

FIG. 16F illustrates an example schematic of a circuit for a PPG sensorusing a photodiode offset current generated dynamically by a controlledvoltage source.

FIG. 16G illustrates an example schematic of a circuit for a PPG sensorincluding ambient light removal functionality using a “switchedcapacitor” method.

FIG. 16H illustrates an example schematic of a circuit for a PPG sensorthat uses a photodiode offset current generated by a constant currentsource (this may also be done using a constant voltage source and aresistor).

FIG. 16I illustrates an example schematic of a circuit for a PPG sensorthat includes ambient light removal functionality and differencingbetween consecutive samples.

FIG. 16J illustrates an example schematic of a circuit for ambient lightremoval and differencing between consecutive samples.

Various circuits and concepts related to heart rate measurement using aPPG sensor are discussed in more detail in U.S. Provisional PatentApplication No. 61/946,439, filed Feb. 28, 2014, which was previouslyincorporated herein by reference in the “Cross-Reference to RelatedApplications” section and which is again hereby incorporated byreference with respect to content directed at heart rate measurementswith a PPG sensor and at circuits, methods, and systems for performingsuch measurements, e.g., to compensate for sensor saturation, ambientlight, and skin tone.

Biometric Feedback

Some embodiments of biometric monitoring devices may provide feedback tothe user based on one or more biometric signals. In one embodiment, aPPG signal may be presented to the user as a real-time or near-real-timewaveform on a display of the biometric monitoring device (or on adisplay of a secondary device in communication with the biometricmonitoring device). This waveform may provide similar feedback to thewaveform displayed on an ECG or EKG machine. In addition to providingthe user with an indication of the PPG signal which may be used toestimate various heart metrics (e.g., heart rate), the waveform may alsoprovide feedback that may enable the user to optimize the position andpressure with which they are wearing the biometric monitoring device.For example, the user may see that the waveform has a low amplitude. Inresponse to this, the user may try moving the position of the biometricmonitoring device to a different location which gives a higher amplitudesignal. In some implementations, the biometric monitoring device may,based on such indications, provide instructions to the user to move oradjust the fit of the biometric monitoring device so as to improve thesignal quality.

In another embodiment, feedback about the quality of the PPG signal maybe provided to the user through a method other than displaying thewaveform. The biometric monitoring device may emit an auditory alarm(e.g. a beep) if the signal quality (e.g. signal to noise ratio) exceedsa certain threshold. The biometric monitoring device may provide avisual cue (through the use of a display for example) to the user toeither change the position of the sensor and/or increase the pressurewith which the device is being worn (for example by tightening a wriststrap in the case that the device is worn on the wrist).

Biometric feedback may be provided for sensors other than PPG sensors.For example, if the device uses ECG, EMG, or is connected to a devicewhich performs either of these, it may provide feedback to the userregarding the waveform from those sensors. If the signal-to-noise-ratioof these sensors is low, or the signal quality is otherwise compromised,the user may be instructed on how they can improve the signal. Forexample, if the heart rate cannot be detected from the ECG sensor, thedevice may provide a visual message to the user instructing them to wetor moisten the ECG electrodes to improve the signal.

Environmental Sensors

Some embodiments of biometric monitoring devices of the presentdisclosure may use one, some or all of the following environmentalsensors to, for example, acquire the environmental data, includingenvironmental data outlined in the table below. Such biometricmonitoring devices are not limited to the number or types of sensorsspecified below but may employ other sensors that acquire environmentaldata outlined in the table below. All combinations and permutations ofenvironmental sensors and/or environmental data are intended to fallwithin the scope of the present disclosure. Additionally, the device mayderive environmental data from the corresponding sensor output data, butis not limited to the types of environmental data that it could derivefrom said sensor.

Notably, embodiments of biometric monitoring devices of the presentdisclosure may use one or more, or all of the environmental sensorsdescribed herein and one or more, or all of the physiological sensorsdescribed herein. Indeed, biometric monitoring device of the presentdisclosure may acquire any or all of the environmental data andphysiological data described herein using any sensor now known or laterdeveloped—all of which are intended to fall within the scope of thepresent disclosure.

Environmental Sensors Environmental data acquired Motion DetectorLocation Potential Embodiments: Inertial, Gyroscopic or Accelerometer-based Sensors GPS Pressure/Altimeter sensor Elevation Ambient TempTemperature Light Sensor Indoor vs outdoor Watching TV (spectrum/flickerrate detection) Optical data transfer-initiation, QR codes, etc.Ultraviolet light exposure Audio Indoor vs. Outdoor Compass Locationand/or orientation Potential Embodiments: 3 Axis Compass

In one embodiment, the biometric monitoring device may include analtimeter sensor, for example, disposed or located in the interior ofthe device housing. (See, for example, FIGS. 12B and 12C; FIG. 12Cillustrates an example of a portable biometric monitoring device havingphysiological sensors, environmental sensors, and location sensorsconnected to a processor). In such a case, the device housing may have avent that allows the interior of the device to measure, detect, sampleand/or experience any changes in exterior pressure. In one embodiment,the vent may prevent water from entering the device while facilitatingmeasuring, detecting and/or sampling changes in pressure via thealtimeter sensor. For example, an exterior surface of the biometricmonitoring device may include a vent type configuration or architecture(for example, a Gore™ vent) that allows ambient air to move in and outof the housing of the device (which allows the altimeter sensor tomeasure, detect and/or sample changes in pressure), but reduces,prevents, and/or minimizes water and other liquids from flowing into thehousing of the device.

The altimeter sensor, in one embodiment, may be filled with gel thatallows the sensor to experience pressure changes outside of the gel. Thegel may act as a relatively impervious, incompressible, yet flexible,membrane that transmits external pressure variations to the altimeterwhile physically separating the altimeter (and other internalcomponents) from the outside environment. The use of a gel-filledaltimeter may give the device a higher level of environmental protectionwith or without the use of an environmentally sealed vent. The devicemay have a higher survivability rate with a gel-filled altimeter inlocations including, but not limited to, locations that have highhumidity, clothes washers, dish washers, clothes dryers, a steam room orsauna, a shower, a pool, a bath, and any location where the device maybe exposed to moisture, exposed to liquid, or submerged in liquid.

Sensors Integration/Signal Processing

Some embodiments of the biometric monitoring devices of the presentdisclosure may use data from two or more sensors to calculate thecorresponding physiological or environmental data as seen in the tablebelow (for example, data from two or more sensors may be used incombination to determine metrics such as those listed below). Thebiometric monitoring device may include, but is not limited to, thenumber, types, or combinations of sensors specified below. Additionally,such biometric monitoring devices may derive the included data from thecorresponding sensor combinations, but are not limited to the number ortypes of data that may be calculated from the corresponding sensorcombinations.

Data derived from signal Sensor Integrations processing of multiplesensors Skin Temp and Ambient Temp Heat Flux Heart Rate and MotionElevation gain Motion detector and other user's Users in the proximitymotion detector (linked by wireless communication path) Motion, anyheart rate sensor, Sit/Standing detection galvanic skin response Anyheart rate, heart rate variability Sleep Phase detection sensor,respiration, motion Sleep Apnea detection Any heart rate sensor and/orResting Heart rate wetness sensor, and/or motion Active Heart Ratedetector Heart rate while asleep Heart rate while sedentary Any heartrate detector Early detection of heart problems: Cardiac ArrhythmiaCardiac Arrest Multiple heart rate detectors Pulse transit time Audioand/or strain gauge Typing detection GPS and photoplethysmographyLocation-stress correlation: (PPG) determination of stressful regionsdetermination of low stress regions Activity specific heart rate restingheart rate active heart rate Automatic activity classification andactivity heart rate determination Heart rate, galvanic skin response,User fatigue, for example while accelerometer and respiration exercising

In some embodiments, the biometric monitoring device may also include anear-field communication (NFC) receiver/transmitter to detect proximityto another device, such as a mobile phone. When the biometric monitoringdevice is brought into close or detectable proximity to the seconddevice, it may trigger the start of new functionality on the seconddevice (e.g., the launching of an “app” on the mobile phone and radiosyncing of physiological data from the device to the second device).(See, for example, FIG. 10). Indeed, the biometric monitoring device ofthe present disclosure may implement any of the circuitry and techniquesdescribed and/or illustrated in U.S. Provisional Patent Application61/606,559, filed Mar. 5, 2012, “Near Field Communication System, andMethod of Operating Same”, inventor: James Park (the contents of whichare incorporated herein by reference for such purpose).

FIG. 10 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics. The app may be activatedwhenever the biometric monitoring device comes into proximity of apassive or active NFC tag. This NFC tag may be attached to the user'shandlebars.

In another embodiment, the biometric monitoring device may include alocation sensor (for example, GPS circuitry) and heartbeat waveformsensor (for example, photoplethysmography circuitry) to generate GPS- orlocation-related data and heart rate-related data, respectively. (See,for example, FIGS. 12B and 12C). The biometric monitoring device maythen fuse, process and/or combine data from these twosensors/circuitries to, for example, determine, correlate, and/or “map”geographical regions according to physiological data (for example, heartrate, stress, activity level, quantity of sleep and/or caloric intake).In this way, the biometric monitoring device may identify geographicalregions that increase or decrease a measurable user metric including,but not limited to, heart rate, stress, activity, level, quantity ofsleep and/or caloric intake.

In addition thereto, or in lieu thereof, some embodiments of biometricmonitoring devices may employ GPS-related data andphotoplethysmography-related data (notably, each of which may beconsidered data streams) to determine or correlate the user's heart rateaccording to activity levels—for example, as determined by the user'sacceleration, speed, location and/or distance traveled (as measured bythe GPS and/or determined from GPS-related data). (See, for example,FIGS. 12B and 12C). Here, in one embodiment, heart rate as a function ofspeed may be “plotted” for the user, or the data may be broken down intodifferent levels including, but not limited to, sleeping, resting,sedentary, moderately active, active, and highly active.

Indeed, some embodiments of biometric monitoring devices may alsocorrelate GPS-related data to a database of predetermined geographiclocations that have activities associated with them for a set ofpredetermined conditions. For example, activity determination andcorresponding physiological classification (for example, heart rateclassification) may include correlating a user's GPS coordinates thatcorrespond to location(s) of exercise equipment, health club and/or gymand physiological data. Under these circumstances, a user's heart rateduring, for example a gym workout, may be automatically measured anddisplayed. Notably, many physiological classifications may be based onGPS-related data including location, acceleration, altitude, distanceand/or velocity. Such a database including geographic data andphysiological data may be compiled, developed and/or stored on thebiometric monitoring device and/or external computing device. Indeed, inone embodiment, the user may create their own location database or addto or modify the location database to better classify their activities.

In another embodiment, the user may simultaneously wear multiplebiometric monitoring devices (having any of the features describedherein). The biometric monitoring devices of this embodiment maycommunicate with each other or a remote device using wired or wirelesscircuitry to calculate, for example, biometric or physiologic qualitiesor quantities that, for example, may be difficult or inaccurate tocalculate otherwise, such as pulse transit time. The use of multiplesensors may also improve the accuracy and/or precision of biometricmeasurements over the accuracy and/or precision of a single sensor. Forexample, having a biometric tracking device on the waist, wrist, andankle may improve the detection of the user taking a step over that of asingle device in only one of those locations. Signal processing may beperformed on the biometric tracking devices in a distributed orcentralized method to provide measurements improved over that of asingle device. This signal processing may also be performed remotely andcommunicated back to the biometric tracking devices after processing.

In another embodiment, heart rate or other biometric data may becorrelated to a user's food log (a log of foods ingested by a user,their nutritional content, and portions thereof). Food log entries maybe entered into the food log automatically or may be entered by the userthemselves through interaction with the biometric monitoring device (ora secondary or remote device, e.g., a smartphone, in communication withthe biometric monitoring device or some other device, e.g., a server, incommunication with the biometric monitoring device). Information may bepresented to the user regarding the biometric reaction of their body toone or more food inputs. For example, if a user has coffee, their heartrate may rise as a result of the caffeine. In another example, if a userhas a larger portion of food late at night, it may take longer for themto fall asleep than usual. Any combination of food input andcorresponding result in biometrics may be incorporated into such afeedback system.

The fusion of food intake data and biometric data may also enable someembodiments of biometric monitoring device to make an estimation of auser's glucose level. This may be particularly useful for users who havediabetes. With an algorithm which relates the glucose level to theuser's activity (e.g. walking, running, calorie burn) and nutritionalintake, a biometric monitoring device may be able to advise the userwhen they are likely to have an abnormal blood sugar level.

Processing Task Delegation

Embodiments of biometric monitoring devices may include one or moreprocessors. Figures. For example, an independent application processormay be used to store and execute applications that utilize sensor dataacquired and processed by one or more sensor processors (processor(s)that process data from physiological, environmental, and/or activitysensors). In the case where there are multiple sensors, there may alsobe multiple sensor processors. An application processor may have sensorsdirectly connected to it as well. Sensor and application processors mayexist as separate discrete chips or exist within the same packaged chip(multi-core). A device may have a single application processor, or anapplication processor and sensor processor, or a plurality ofapplication processors and sensor processors.

In one embodiment, the sensor processor may be placed on a daughterboardthat consists of all of the analog components. This board may have someof the electronics typically found on the main PCB such as, but notlimited to, transimpedance amplifiers, filtering circuits, levelshifters, sample-and-hold circuits, and a microcontroller unit. Such aconfiguration may allow the daughterboard to be connected to the mainPCB through the use of a digital connection rather than an analogconnection (in addition to any necessary power or ground connections). Adigital connection may have a variety of advantages over an analogdaughterboard to main PCB connection, including, but not limited to, areduction in noise and a reduction in the number of necessary cables.The daughterboard may be connected to the main board through the use ofa flex cable or set of wires.

Multiple applications may be stored on an application processor. Anapplication may consist of executable code and data for the application,but is not limited to these. Data may consist of graphics or otherinformation required to execute the application or it may be informationoutput generated by the application. The executable code and data forthe application may both reside on the application processor (or memoryincorporated therein) or the data for the application may be stored andretrieved from an external memory. External memory may include but isnot limited to NAND flash, NOR flash, flash on another processor, othersolid-state storage, mechanical or optical disks, RAM, etc.

The executable code for an application may also be stored in an externalmemory. When a request to execute an application is received by theapplication processor, the application processor may retrieve theexecutable code and/or data from the external storage and execute it.The executable code may be temporarily or permanently stored on thememory or storage of the application processor. This allows theapplication to be executed more quickly on the next execution request,since the step of retrieval is eliminated. When the application isrequested to be executed, the application processor may retrieve all ofthe executable code of the application or portions of the executablecode. In the latter case, only the portion of executable code requiredat that moment is retrieved. This allows applications that are largerthan the application processor's memory or storage to be executed.

The application processor may also have memory protection features toprevent applications from overwriting, corrupting, interrupting,blocking, or otherwise interfering with other applications, the sensorsystem, the application processor, or other components of the system.

Applications may be loaded onto the application processor and/or anyexternal storage via a variety of wired, wireless, optical, orcapacitive mechanisms including, but not limited to, USB, Wi-Fi,Bluetooth, Bluetooth Low Energy, NFC, RFID, Zigbee.

Applications may also be cryptographically signed with an electronicsignature. The application processor may restrict the execution ofapplications to those that have the correct signature.

Integration of Systems in a Biometric Monitoring Device

In some implementations of biometric monitoring devices, some sensors orelectronic systems in the biometric monitoring device may be integratedwith one another or may share components or resources. For example, aphotodetector for an optically-based heartbeat waveform sensor (such asmay be used in the heart-rate sensors discussed in U.S. ProvisionalPatent Application No. 61/946,439, filed Feb. 28, 2014, and previouslyincorporated by reference herein, may also serve as a photodetector fordetermining ambient light level, such as may be used to correct for theeffects of ambient light on the heartbeat waveform sensor reading. Forexample, if the light source for such a heart rate detector is turnedoff, the light that is measured by the photodetector may be indicativeof the amount of ambient light that is present.

In some implementations of a biometric monitoring device, the biometricmonitoring device may be configured or communicated with using onboardoptical sensors such as the components in an optical heart rate monitor.For example, the photodetectors of an optical heart-rate sensor (or, ifpresent, an ambient light sensor) may also serve as a receiver for anoptically-based transmission channel, e.g., infrared communications.

In some implementations of a biometric monitoring device, a hybridantenna may be included that combines a radio frequency antenna, e.g., aBluetooth antenna or GPS antenna, with an inductive loop, such as may beused in a near-field communications (NFC) tag or in an inductivecharging system. In such implementations, the functionality for twodifferent systems may be provided in one integrated system, savingpacking volume. In such a hybrid antenna, an inductive loop may beplaced in close proximity to the radiator of an inverted-F antenna. Theinductive loop may inductively couple with the radiator, allowing theinductive loop to serve as a planar element of the antenna forradio-frequency purposes, thus forming, for example, a planar inverted-Fantenna. At the same time, the inductive loop may also serve its normalfunction, e.g., such as providing current to an NFC chip throughinductive coupling with an electromagnetic field generated by an NFCreader. Examples of such hybrid antenna systems are discussed in moredetail in U.S. Provisional Patent Application No. 61/948,470, filed Mar.5, 2014, which was previously incorporated herein by reference in the“Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed athybrid antenna structures. Of course, such hybrid antennas may also beused in other electronic devices other than biometric monitoringdevices, and such non-biometric-monitoring-device use of hybrid antennasis contemplated as being within the scope of this disclosure.

Methods of Wearing the Device

Some embodiments of biometric monitoring devices may include a housinghaving a size and shape that facilitates fixing the biometric monitoringdevice to the user's body during normal operation wherein the device,when coupled to the user, does not measurably or appreciably impact theuser's activity. The biometric monitoring device may be worn indifferent ways depending on the specific sensor package that isintegrated into the biometric monitoring device and the data that theuser would like to acquire.

A user may wear some embodiments of the biometric monitoring devices ofthe present disclosure on their wrist or ankle (or arm or leg) with theuse of a band that is flexible and thereby readily fitted to the user.The band may have an adjustable circumference, therefore allowing it tobe fitted to the user. The band may be constructed from a material thatshrinks when exposed to heat, therefore allowing the user to create acustom fit. The band may be detachable from the “electronics” portion ofthe biometric monitoring device and, if necessary, replaceable.

In some embodiments, the biometric monitoring device may consist of twomajor components—a body (containing the “electronics”) and a band (thatfacilitates attaching the device to the user). The body may include ahousing (made, for example, of a plastic or plastic-like material) andextension tabs projecting from the body (made, for example, from a metalor metal-like material). (See, for example, FIGS. 2C through 3C). Theband (made, for example, of a thermoplastic urethane) may be attachableto the body, e.g., mechanically or adhesively. The band may extend out afraction of the circumference of the user's wrist. The distal ends ofthe urethane band may be connected with a Velcro or a hook-and-loopelastic fabric band that loops around a D-Ring on one side and thenattaches back to itself. In this embodiment, the closure mechanism mayallow the user infinite band length adjustment (unlike an indexed holeand mechanical clasp closure). The Velcro or elastic fabric may beattached to the band in a manner that allows it to be replaced (forexample, if it is worn or otherwise undesirable to wear before theuseful end of life of the device). In one embodiment, the Velcro orfabric may be attached with screws or rivets and/or glue, adhesives,and/or a clasp to the band.

Embodiments of the biometric monitoring devices of the presentdisclosure may also be integrated into and worn in a necklace, chestband, bra, adhesive patch, glasses, earring, or toe band. Such biometricmonitoring devices may be built in such a way that the sensorpackage/portion of the biometric monitoring device is removable and maybe worn in any number of ways including, but not limited to, thoselisted above.

In another embodiment, embodiments of biometric monitoring devices ofthe present disclosure may be worn clipped to an article of clothing ordeposited in clothing (e.g., pocket) or an accessory (e.g., handbag,backpack, wallet). Because such biometric monitoring devices may not benear the user's skin, in embodiments that include heart ratemeasurements, the measurements may be obtained in a discrete, “ondemand” context by the user manually placing the device into a specificmode (e.g., by depressing a button, covering a capacitive touch sensorwith a fingertip, etc., possibly with the heartbeat waveform sensorembedded in the button/sensor) or automatically once the user places thedevice against the skin (e.g., applying the finger to an opticalheartbeat waveform sensor).

User Interface with the Device

Some embodiments of a biometric monitoring device may includefunctionality for allowing one or more methods of interacting with thedevice either locally or remotely.

In some embodiments, the biometric monitoring device may convey datavisually through a digital display. The physical embodiment of thisdisplay may use any one or a plurality of display technologiesincluding, but not limited to one or more of LED, LCD, AMOLED, E-Ink,Sharp display technology, graphical displays, and other displaytechnologies such as TN, HTN, STN, FSTN, TFT, IPS, and OLET. Thisdisplay may show data acquired or stored locally on the device or maydisplay data acquired remotely from other devices or Internet services.The biometric monitoring device may use a sensor (for example, anAmbient Light Sensor, “ALS”) to control or adjust the amount of screenbacklighting, if backlighting is used. For example, in dark lightingsituations, the display may be dimmed to conserve battery life, whereasin bright lighting situations, the display brightness may be increasedso that it is more easily read by the user.

In another embodiment, the biometric monitoring device may use single ormulticolor LEDs to indicate a state of the device. States that thebiometric monitoring device may indicate using LEDs may include, but arenot limited to, biometric states such as heart rate or applicationstates such as an incoming message or that a goal has been reached.These states may be indicated through the LED's color, the LED being onor off (or in an intermediate intensity), pulsing (and/or rate thereof)of the LEDs, and/or a pattern of light intensities from completely offto highest brightness. In one embodiment, an LED may modulate itsintensity and/or color with the phase and frequency of the user's heartrate.

In some embodiments, the use of an E-Ink display may allow the displayto remain on without the battery drain of a non-reflective display. This“always-on” functionality may provide a pleasant user experience in thecase of, for example, a watch application where the user may simplyglance at the biometric monitoring device to see the time. The E-Inkdisplay always displays content without comprising the battery life ofthe device, allowing the user to see the time as they would on atraditional watch.

Some implementations of a biometric monitoring device may use a lightsuch as an LED to display the heart rate of the user by modulating theamplitude of the light emitted at the frequency of the user's heartrate. The device may depict heart rate zones (e.g., aerobic, anaerobic,etc.) through the color of an LED (e.g., green, red) or a sequence ofLEDs that light up in accordance with changes in heart rate (e.g., aprogress bar). The biometric monitoring device may be integrated orincorporated into another device or structure, for example, glasses orgoggles, or communicate with glasses or goggles to display thisinformation to the user.

Some embodiments of a biometric monitoring device may also conveyinformation to a user through the physical motion of the device. Onesuch embodiment of a method to physically move the device is the use ofa vibration-inducing motor. The device may use this method alone, or incombination with a plurality of other motion-inducing technologies.

In some implementations, a biometric monitoring device may conveyinformation to a user through audio feedback. For example, a speaker inthe biometric monitoring device may convey information through the useof audio tones, voice, songs, or other sounds.

These three information communication methods—visual, motion, andauditory—may, in various embodiments of biometric monitoring devices, beused alone or in any combination with each other or another method ofcommunication to communicate any one or plurality of the followinginformation:

That a user needs to wake up at certain time

That a user should wake up as they are in a certain sleep phase

That a user should go to sleep as it is a certain time

That a user should wake up as they are in a certain sleep phase and in apreselected time window bounded by the earliest and latest time that theuser wants to wake up.

That an email was received

That the user has been inactive for a certain period of time. Notably,this may integrate with other applications like, for instance, a meetingcalendar or sleep tracking application to block out, reduce, or adjustthe behavior of the inactivity alert.

That the user has been active for a certain period of time

That the user has an appointment or calendar event

That the user has reached a certain activity metric

That the user has gone a certain distance

That the user has reached a certain mile pace

That the user has reached a certain speed

That the user has accumulated a certain elevation gain

That the user has taken a certain number of steps

That the user has had a heart rate measurement recently

That the user's heart rate has reached a certain level

That the user has a normal, active, or resting heart rate of a specificvalue or in a specific range

That the user's heart rate has enter or exited a certain goal range ortraining zone

That the user has a new heart rate “zone” goal to reach, as in the caseof heart rate zone training for running, bicycling, swimming, etc.activities

That the user has swum a lap or completed a certain number of laps in apool

An external device has information that needs to be communicated to theuser such as an incoming phone call or any one of the above alerts

That the user has reached a certain fatigue goal or limit. In oneembodiment, fatigue may be determined through a combination of heartrate, galvanic skin response, motion sensor, and/or respiration data

These examples are provided for illustration and are not intended tolimit the scope of information that may be communicated by suchembodiments of biometric monitoring devices (for example, to the user).Note that the data used to determine whether or not an alert conditionis met may be acquired from a first device and/or one or more secondarydevices. The biometric monitoring device itself may determine whetherthe criteria or conditions for an alert have been met. Alternatively, acomputing device in communication with the biometric monitoring device(e.g., a server and/or a mobile phone) may determine when the alertshould occur. In view of this disclosure, other information that thebiometric monitoring device may communicate to the user may beenvisioned by one of ordinary skill in the art. For example, thebiometric monitoring device may communicate with the user when a goalhas been met. The criteria for meeting this goal may be based onphysiological, contextual, and environmental sensors on a first device,and/or other sensor data from one or more secondary devices. The goalmay be set by the user or may be set by the biometric monitoring deviceitself and/or another computing device in communication with thebiometric monitoring device (e.g. a server). In an example embodiment,the biometric monitoring device may vibrate when a biometric goal ismet.

Some embodiments of biometric monitoring devices of the presentdisclosure may be equipped with wireless and/or wired communicationcircuitry to display data on a secondary device in real time. Forexample, such biometric monitoring devices may be able to communicatewith a mobile phone via Bluetooth Low Energy in order to give real-timefeedback of heart rate, heart rate variability, and/or stress to theuser. Such biometric monitoring devices may coach or grant “points” forthe user to breathe in specific ways that alleviate stress (e.g. bytaking slow, deep breaths). Stress may be quantified or evaluatedthrough heart rate, heart rate variability, skin temperature, changes inmotion-activity data and/or galvanic skin response.

Some embodiments of biometric monitoring devices may receive input fromthe user through one or more local or remote input methods. One suchembodiment of local user input may use a sensor or set of sensors totranslate a user's movement into a command to the device. Such motionscould include but may not be limited to tapping, rolling the wrist,flexing one or more muscles, and swinging one's arm. Another user inputmethod may be through the use of a button such as, but not limited to,capacitive touch buttons, capacitive screen buttons, and mechanicalbuttons. In one embodiment, the user interface buttons may be made ofmetal. In embodiments where the screen uses capacitive touch detection,it may always be sampling and ready to respond to any gesture or inputwithout an intervening event such as pushing a physical button. Suchbiometric monitoring devices may also take input through the use ofaudio commands. All of these input methods may be integrated intobiometric monitoring devices locally or integrated into a remote devicethat can communicate with such biometric monitoring devices, eitherthrough a wired or wireless connection. In addition, the user may alsobe able to manipulate the biometric monitoring device through a remotedevice. In one embodiment, this remote device may have Internetconnectivity.

Alarms

In some embodiments, the biometric monitoring device of the presentdisclosure may act as a wrist-mounted vibrating alarm to silently wakethe user from sleep. Such biometric monitoring devices may track theuser's sleep quality, waking periods, sleep latency, sleep efficiency,sleep stages (e.g., deep sleep vs REM), and/or other sleep-relatedmetrics through one or a combination of heart rate, heart ratevariability, galvanic skin response, motion sensing (e.g.,accelerometer, gyroscope, magnetometer), and skin temperature. The usermay specify a desired alarm time or window of time (e.g., set alarm togo off between 7 am and 8 am). Such embodiments may use one or more ofthe sleep metrics to determine an optimal time within the alarm windowto wake the user. In one embodiment, when the vibrating alarm is active,the user may cause it to hibernate or turn off by slapping or tappingthe device (which is detected, for example, via motion sensor(s), apressure/force sensor, and/or capacitive touch sensor in the device). Inone embodiment, the device may attempt to arouse the user at an optimumpoint in the sleep cycle by starting a small vibration at a specificuser sleep stage or time prior to the alarm setting. It mayprogressively increase the intensity or noticeability of the vibrationas the user progresses toward wakefulness or toward the alarm setting.(See, for example, FIG. 8).

FIG. 8 illustrates functionality of an example portable biometricmonitoring device smart alarm feature. The biometric monitoring devicemay be able to detect or may be in communication with a device that candetect the sleep stage or state of a user (e.g., light or deep sleep).The user may set a window of time which they would like to be awoken(e.g., 6:15 am to 6:45 am). The smart alarm may be triggered by the usergoing into a light sleep state during the alarm window.

The biometric monitoring device may be configured to allow the user toselect or create an alarm vibration pattern of their choice. The usermay have the ability to “snooze” or postpone an alarm event. In oneembodiment, the user may be able to set the amount of delay for the“snooze” feature—the delay being the amount of time before the alarmwill go off again. They may also be able to set how many times thesnooze feature may be activated per alarm cycle. For example, a user maychoose a snooze delay of 5 minutes and a maximum sequential snoozenumber to be 3. Therefore, they can press snooze up to 3 times to delaythe alarm by 5 minutes each time they press snooze to delay the alarm.In such embodiments, the snooze function will not turn off the alarm ifthe user attempts to press snooze a fourth time.

Some biometric monitoring devices may have information about the user'scalendar and/or schedule. The user's calendar information may be entereddirectly into the biometric monitoring device or it may be downloadedfrom a different device (e.g. a smartphone). This information may beused to automatically set alarms or alarm characteristics. For example,if a user has a meeting at 9 am in the morning, the biometric monitoringdevice may automatically wake the user up at 7:30 am to allow the userenough time to prepare for and/or get to the meeting. The biometricmonitoring device may determine the amount of time required for the userto prepare for the meeting based on the user's current location, thelocation of the meeting, and the amount of time it would take to get thelocation of the meeting from the user's current location. Alternatively,historical data about how long the user takes to get to the meetinglocation and/or prepare to leave for the meeting (e.g. how long it takesto wake up, take a shower, have breakfast, etc. in the morning) may beused to determine at what time to wake the user. A similar functionalitymay be used for calendar events other than meetings such as eatingtimes, sleeping times, napping times, and exercise times.

In some embodiments, the biometric monitoring device may use informationon when the user went to sleep to determine when an alarm should go offto wake the user. This information may supplement calendar informationdescribed herein. The user may have a goal of approximately how manyhours of sleep they would like to get each night or week. The biometricmonitoring device may set the morning alarm at the appropriate time forthe user to meet these sleep goals. In addition to amount of time thatthe user would like to sleep each night, other sleep goals that the usermay set may include, but are not limited to, the amount of deep sleep,REM sleep, and light sleep that the user experiences while sleeping, allof which may be used by the biometric monitoring device to determinewhen to set an alarm in the morning. Additionally, the user may bealerted at night when they should go to bed to meet their sleep goals.Additionally, the user may be alerted during the day when they shouldtake a nap to meet their sleep goals. The time at which to alert a userthat they should take a nap may be determined by factors that optimizethe user's sleep quality during the nap, subsequent naps, or night-timesleep. For example, the user is likely to have a hard time fallingasleep at night if they took a nap in the early evening. The user mayalso be advised to eat certain foods or drinks or avoid certain foods ordrinks to optimize their sleep quality. For example, a user may bediscouraged from drinking alcohol close to their bed time as it islikely to decrease their sleep quality. The user may also be advised toperform certain activities or avoid certain activities to optimize theirsleep quality. For example, a user may be encouraged to exercise in theearly afternoon to improve their sleep quality. A user may bediscouraged from exercising or watching TV close to their bedtime toimprove their sleep quality.

User Interface with a Secondary Device

In some embodiments, the biometric monitoring device may transmit andreceive data and/or commands to and/or from a secondary electronicdevice. The secondary electronic device may be in direct or indirectcommunication with the biometric monitoring device. Direct communicationrefers herein to the transmission of data between a first device and asecondary device without any intermediary devices. For example, twodevices may communicate to one another over a wireless connection (e.g.Bluetooth) or a wired connection (e.g. USB). Indirect communicationrefers to the transmission of data between a first device and asecondary device with the aid of one or multiple intermediary thirddevices which relay the data. Third devices may include, but are notlimited to, a wireless repeater (e.g. WiFi repeater), a computing devicesuch as a smartphone, laptop, desktop or tablet computer, a cell phonetower, a computer server, and other networking electronics. For example,a biometric device may send data to a smartphone which forwards the datathrough a cellular network data connection to a server which isconnected through the internet to the cellular network.

In some embodiments, the secondary device that acts as a user interfaceto the biometric monitoring device may consist of a smartphone. An appon the smart phone may facilitate and/or enable the smartphone to act asa user interface to the biometric monitoring device. The biometricmonitoring device may send biometric and other data to the smartphone inreal-time or with some delay. The smartphone may send a command orcommands to the biometric monitoring device, for example, to instruct itto send biometric and other data to the smartphone in real-time or withsome delay. For example, if the user enters a mode in the app fortracking a run, the smartphone may send a command to the biometricdevice to instruct it to send data in real-time. Therefore, the user cantrack their run on their app as they go along without any delay.

Such a smartphone may have one or multiple apps to enable the user toview data from their biometric device or devices. The app may, bydefault, open to a “dashboard” page when the user launches or opens theapp. On this page, summaries of data totals such as the total number ofsteps, floors climbed miles traveled, calories burned, calories consumedand water consumed may be shown. Other pertinent information such as thelast time the app received data from the biometric monitoring device,metrics regarding the previous night's sleep (e.g. when the user went tosleep, woke up, and how long they slept for), and how many calories theuser can eat in the day to maintain their caloric goals (e.g. a caloriedeficit goal to enable weight loss) may also be shown. The user may beable to choose which of these and other metrics are shown on thedashboard screen. The user may be able to see these and other metrics onthe dashboard for previous days. They may be able to access previousdays by pressing a button or icon on a touchscreen. Alternatively,gestures such as swiping to the left or right may enable the user tonavigate through current and previous metrics.

The smartphone app may also have another page which provides a summaryof the user's activities. Activities may include, but are not limitedto, walking, running, biking, cooking, sitting, working, swimming,working out, weightlifting, commuting, and yoga. Metrics pertinent tothese activities may be presented on this page. For example, a bar graphmay show how the number of steps the user took for different portions ofthe day (e.g. how many steps every 5 minutes or 1 hour). In anotherexample, the amount of time the user spent performing a certain activityand how many calories were burned in this period of time may bedisplayed. Similar to the dashboard page, the app may providenavigational functionality to allow the user to see these and othermetrics for past days. Other time periods such as an hour, minute, week,month or year may also be selected by the user to enable them to viewtrends and metrics of their activities over shorter or larger spans oftime.

The smartphone app may also have an interface to log food that has been,or will be, eaten by the user. This interface may have a keyword searchfeature to allow the user to quickly find the food that they would liketo enter into their log. As an alternative to, or in addition to,searching for foods, users may have the ability to find a food to log bynavigating through a menu or series of menus. For example, a user maychoose the following series ofcategories—breakfast/cereal/healthy/oatmeal to arrive at the food whichthey would like to log (e.g., apple-flavored oatmeal). At any one ofthese menus, the user may be able to perform a keyword search. Forexample, the user may search for “oatmeal” after having selected thecategory “breakfast” to search for the keyword “oatmeal” within thecategory of breakfast foods. After having selected the food that theywould like to log, the user may be able to modify or enter the servingsize and nutritional content. After having logged at least one food, theapp may display a summary of the foods that were logged in a certaintime period (e.g. a day) and the nutritional content of the foods(individual and total calorie content, vitamin content, sugar content,etc.).

The smartphone app may also have a page that displays metrics regardingthe user's body such as the user's weight, body fat percentage, BMI, andwaist size. It may display a graph or graphs showing the trend of one ormultiple of these metrics over a certain period of time (e.g., twoweeks). The user may be able to choose the value of this period of timeand view previous time periods (e.g., last month).

The smartphone app may also a page which allows the user to enter howmuch water the user has consumed. Each time the user drinks some water,they may enter that amount in the unit of their choice (e.g., ozs.,cups, etc.). The app may display the total of all of the water the userhas logged within a certain time period (e.g., a day). The app may allowthe user to see previously-logged water entries and daily totals forprevious days as well as the current day.

The smartphone app may also have a page that displays online friends ofthe user. This “friends” page may enable the user to add or request newfriends (e.g., by searching for their name or by their email address).This page may also display a leaderboard of the user and his or herfriends. The user and his or friends may be ranked based on one or moremetrics. For example, the user and his or her friends may be rankedusing the total of the past seven days' step counts.

The smartphone app may also have a page that shows metrics regarding theuser's sleep for the previous night and/or previous nights. This pagemay also enable the user to log when they slept in the past byspecifying when they went to bed and when they woke. The user may alsohave the ability to enter a subjective metric about their sleep (e.g.,bad night's rest, good night's rest, excellent night's rest, etc.). Theuser may be able to view these metrics for days or time periods (e.g.,two weeks) in the past. For example, the sleep page may default toshowing a bar graph of the amount of time the user slept each night inthe last two weeks. The user may be able to also view a bar graph of theamount of time the user slept each night in the last month.

The user may also be able to access the full capabilities of thesmartphone app described herein (e.g., the ability to enter food logs,view dashboard, etc.) through an alternative or additional interface. Inone embodiment, this alternative interface may consist of a webpage thatis hosted by a server in indirect communication with the biometricmonitoring device. The webpage may be accessed through any internetconnected device using a program such as a web browser.

Wireless Connectivity and Data Transmission

Some embodiments of biometric monitoring devices of the presentdisclosure may include a means of wireless communication to transmit andreceive information from the Internet and/or other devices. The wirelesscommunication may consist of one or more interfaces such as Bluetooth,ANT, WLAN, power-line networking, and cell phone networks. These areprovided as examples and should not be understood to exclude otherexisting wireless communication methods or protocols, or wirelesscommunications techniques or protocols that are yet to be invented.

The wireless connection may be bi-directional. The biometric monitoringdevice may transmit, communicate and/or push its data to other devices,e.g., smart phones, computers, etc., and/or the Internet, e.g., webservers and the like. The biometric monitoring device may also receive,request and/or pull data from other devices and/or the Internet.

The biometric monitoring device may act as a relay to providecommunication for other devices to each other or to the Internet. Forexample, the biometric monitoring device may connect to the Internet viaWLAN but also be equipped with an ANT radio. An ANT device maycommunicate with the biometric monitoring device to transmit its data tothe Internet through the biometric monitoring device's WLAN (and viceversa). As another example, the biometric monitoring device may beequipped with Bluetooth. If a Bluetooth-enabled smart phone comes withinrange of the biometric monitoring device, the biometric monitoringdevice may transmit data to, or receive data from, the Internet throughthe smart phone's cell phone network. Data from another device may alsobe transmitted to the biometric monitoring device and stored (or viceversa) or transmitted at a later time.

Embodiments of biometric monitoring devices of the present disclosuremay also include functionality for streaming or transmitting web contentfor display on the biometric monitoring device. The following aretypical examples of such content:

1. Historical graphs of heart rate and/or other data measured by thedevice but stored remotely2. Historical graphs of user activity and/or foods consumed and/or sleepdata that are measured by other devices and/or stored remotely (e.g.,such as at a website like fitbit.com)3. Historical graphs of other user-tracked data that are storedremotely. Examples include heart rate, blood pressure, arterialstiffness, blood glucose levels, cholesterol, duration of TV watching,duration of video game play, mood, etc.4. Coaching and/or dieting data based on one or more of the user's heartrate, current weight, weight goals, food intake, activity, sleep, andother data.5. User progress toward heart rate, weight, activity, sleep, and/orother goals.6. Summary statistics, graphics, badges, and/or metrics (e.g., “grades”)to describe the aforementioned data7. Comparisons between the aforementioned data for the user and similardata for his/her “friends” with similar devices and/or tracking methods8. Social content such as Twitter feeds, instant messaging, and/orFacebook updates9. Other online content such as newspaper articles, horoscopes, weatherreports, RSS feeds, comics, crossword puzzles, classifiedadvertisements, stock reports, and websites10. Email messages and calendar schedules

Content may be delivered to the biometric monitoring device according todifferent contexts. For instance, in the morning, news and weatherreports may be displayed along with the user's sleep data from theprevious night. In the evening, a daily summary of the day's activitiesmay be displayed.

Various embodiments of biometric monitoring devices as disclosed hereinmay also include NFC, RFID, or other short-range wireless communicationcircuitry that may be used to initiate functionality in other devices.For instance, a biometric monitoring device may be equipped with an NFCantenna so that when a user puts it into close proximity with a mobilephone, an app is launched automatically on the mobile phone.

These examples are provided for illustration and are not intended tolimit the scope of data that may be transmitted, received, or displayedby the device, nor any intermediate processing that may occur duringsuch transfer and display. In view of this disclosure/application, manyother examples of data that may be streamed to or via a biometricmonitoring device may be envisioned by one reasonably skilled in theart.

Charging and Data Transmission

Some embodiments of biometric monitoring devices may use a wiredconnection to charge an internal rechargeable battery and/or transferdata to a host device such as a laptop or mobile phone. In oneembodiment, similar to one discussed earlier in this disclosure, thebiometric monitoring device may use magnets to help the user align thebiometric monitoring device to a dock or cable. The magnetic field ofmagnets in the dock or cable and the magnets in the device itself may bestrategically oriented so as to force the biometric monitoring device toself-align with the dock or cable (or, more specifically, a connector onthe cable) and so as to provide a force that holds the biometricmonitoring device in the dock or to the cable. The magnets may also beused as conductive contacts for charging or data transmission purposes.In another embodiment, a permanent magnet may only be used in the dockor cable side and not in the biometric monitoring device itself. Thismay improve the performance of the biometric monitoring device where thebiometric monitoring device employs a magnetometer. If there is a magnetin the biometric monitoring device, the strong field of a nearbypermanent magnet may make it significantly more difficult for themagnetometer to accurately measure the earth's magnetic field. In suchembodiments, the biometric monitoring device may utilize a ferrousmaterial in place of a magnet, and the magnets on the dock or cable sidemay attach to the ferrous material.

In another embodiment, the biometric monitoring device may contain oneor more electromagnets in the biometric monitoring device body. Thecharger or dock for charging and data transmission may also contain anelectromagnet and/or a permanent magnet. The biometric monitoring devicecould only turn on its electromagnet when it is close to the charger ordock. The biometric monitoring device may detect proximity to the dockor charger by looking for the magnetic field signature of a permanentmagnet in the charger or dock using a magnetometer. Alternatively, thebiometric monitoring device may detect proximity to the charger bymeasuring the Received Signal Strength Indication (RSSI) of a wirelesssignal from the charger or dock, or, in some embodiments, by recognizingan NFC or RFID tag associated with the charger or dock. Theelectromagnet could be reversed, creating a force that repels the devicefrom the charging cable or dock either when the device doesn't need tobe charged, synced, or when it has completed syncing or charging. Insome embodiments, the charger or dock may include the electromagnet andmay be configured (e.g., a processor in the charger or dock may beconfigured via program instructions) to turn the electromagnet on when abiometric monitoring device is connected for charging (the electromagnetmay normally be left on such that a biometric monitoring device that isplaced on the charger is drawn against the charger by the electromagnet,or the electromagnet may be left off until the charger determines that abiometric monitoring device has been placed on the charger, e.g.,through completion of a charging circuit, recognition of an NFC tag inthe biometric monitoring device, etc., and then turned on to draw thebiometric monitoring device against the charger. Upon completion ofcharging (or of data transfer, if the charger is actually a datatransfer cradle or a combined charger/data transfer cradle), theelectromagnet may be turned off (either temporarily or until thebiometric monitoring device is again detected as being placed on thecharger) and the biometric monitoring device may stop being drawnagainst the charger. In such embodiments, it may be desirable to orientthe interface between the biometric monitoring device and the chargersuch that, in the absence of a magnetic force generated by theelectromagnet, the biometric monitoring device would fall off of thecharger or otherwise shift into a visibly different position from thecharging position (to visually indicate to a user that charging or datatransfer is complete).

Sensor Use in Data Transfer

In some implementations, biometric monitoring devices may include acommunications interface that may switch between two or more protocolsthat have different data transmission rates and different powerconsumption rates. Such switching may be driven by data obtained fromvarious sensors of the biometric monitoring device. For example, ifBluetooth is used, the communications interface may switch between usingBluetooth base rate/enhanced data rate (BR/EDR) and Bluetooth low energy(BLE) protocols responsive to determinations made based on data from thesensors of the biometric monitoring device. For example, thelower-power, slower BLE protocol may be used when sensor data fromaccelerometers in a biometric monitoring device indicates that thewearer is asleep or otherwise sedentary. By contrast, the higher-power,faster BR/EDR protocol may be used when sensor data from theaccelerometers in a biometric monitoring device indicates that thewearer is walking around. Such adaptive data transmission techniques andfunctionality are discussed further in U.S. Provisional PatentApplication No. 61/948,468, filed Mar. 5, 2014, which was previouslyincorporated herein by reference in the “Cross-Reference to RelatedApplications” section and which is again hereby incorporated byreference with respect to content directed at adaptive data transferrates in biometric monitoring devices.

Such communication interfaces may also serve as a form of sensor for abiometric monitoring device. For example, a wireless communicationsinterface may allow a biometric monitoring device to determine thenumber and type of devices that are within range of the wirelesscommunications interface. Such data may be used to determine if thebiometric monitoring device is in a particular context, e.g., indoors,in a car, etc., and to change its behavior in various ways in responseto such a determination. For example, as discussed in U.S. ProvisionalPatent Application No. 61/948,468 (incorporated by reference above),such contexts may be used to drive the selection of a particularwireless communications protocol to use for wireless communications.

Configurable App Functionality

In some embodiments, biometric monitoring devices of the presentdisclosure may include a watch-like form factor and/or a bracelet,armlet, or anklet form factor and may be programmed with “apps” thatprovide specific functionality and/or display specific information. Appsmay be launched or closed by a variety of means including, but notlimited to, pressing a button, using a capacitive touch sensor,performing a gesture that is detected by an accelerometer, moving to aspecific location or area detected by a GPS or motion sensor,compressing the biometric monitoring device body (thereby creating apressure signal inside the device that may be detected by an altimeterinside the biometric monitoring device), or placing the biometricmonitoring device close to an NFC tag that is associated with an app orset of apps. Apps may also be automatically triggered to launch or closeby certain environmental or physiological conditions including, but notlimited to, detection of a high heart rate, detection of water using awet sensor (to launch a swimming application, for example), a certaintime of day (to launch a sleep tracking application at night, forexample), a change in pressure and motion characteristic of a planetaking off or landing to launch and close an “airplane” mode app. Appsmay also be launched or closed by meeting multiple conditionssimultaneously. For example, if an accelerometer detects that a user isrunning and the user presses a button, the biometric monitoring devicemay launch a pedometer application, an altimeter data collectionapplication, and/or display. In another case where the accelerometerdetects swimming and the user presses the same button, it may launch aswimming lap-counting application.

In some embodiments, the biometric monitoring device may have aswim-tracking mode that may be launched by starting a swimming app. Inthis mode, the biometric monitoring device's motion sensors and/ormagnetometer may be used to detect swim strokes, classify swim stroketypes, detect swimming laps, and other related metrics such as strokeefficiency, lap time, speed, distance, and calorie burn. Directionalchanges indicated by the magnetometer may be used to detect a diversityof lap turn methods. In a preferred embodiment, data from a motionsensor and/or pressure sensor may be used to detect strokes.

In another embodiment, a bicycling app may be launched by moving thebiometric monitoring device within proximity of an NFC or RFID tag thatis located on the bicycle, on a mount on the bicycle, or in a locationassociated with a bicycle including, but not limited to, a bike rack orbike storage facility. (See, for example, FIG. 10). The app launched mayuse a different algorithm than is normally used to determine metricsincluding, but not limited to, calories burned, distance travelled, andelevation gained. The app may also be launched when a wireless bikesensor is detected including, but not limited to, a wheel sensor, GPS,cadence sensor, or power meter. The biometric monitoring device may thendisplay and/or record data from the wireless bike sensor or bikesensors.

Additional apps include, but are not limited to, a programmable orcustomizable watch face, stop watch, music player controller (e.g., mp3player remote control), text message and/or email display or notifier,navigational compass, bicycle computer display (when communicating witha separate or integrated GPS device, wheel sensor, or power meter),weight-lifting tracker, sit-up reps tracker, pull up reps tracker,resistance training form/workout tracker, golf swing analyzer, tennis(or other racquet sport) swing/serve analyzer, tennis game swingdetector, baseball swing analyzer, ball throw analyzer (e.g., football,baseball), organized sports activity intensity tracker (e.g., football,baseball, basketball, volleyball, soccer), disk throw analyzer, foodbite detector, typing analyzer, tilt sensor, sleep quality tracker,alarm clock, stress meter, stress/relaxation biofeedback game (e.g.,potentially in combination with a mobile phone that provides auditoryand/or visual cues to train user breathing in relaxation exercises),teeth brushing tracker, eating rate tracker (e.g., to count or track therate and duration by which a utensil is brought to the mouth for foodintake), intoxication or suitability to drive a motor vehicle indicator(e.g., through heart rate, heart rate variability, galvanic skinresponse, gait analysis, puzzle solving, and the like), allergy tracker(e.g., using galvanic skin response, heart rate, skin temperature,pollen sensing and the like (possibly in combination with externalseasonal allergen tracking from, for instance, the internet and possiblydetermining the user's response to particular forms of allergen, e.g.,tree pollen, and alerting the user to the presence of such allergens,e.g., from seasonal information, pollen tracking databases, or localenvironmental sensors in the biometric monitoring device or employed bythe user), fever tracker (e.g., measuring the risk, onset, or progressof a fever, cold, or other illness, possibly in combination withseasonal data, disease databases, user location, and/or user providedfeedback to assess the spread of a particular disease (e.g., flu) inrelation to a user, and possibly prescribing or suggesting theabstinence of work or activity in response), electronic games, caffeineaffect tracker (e.g., monitoring the physiologic response such as heartrate, heart rate variability, galvanic skin response, skin temperature,blood pressure, stress, sleep, and/or activity in either short term orlong term response to the intake or abstinence of coffee, tea, energydrinks and/or other caffeinated beverages), drug affect tracker (e.g.,similar to the previously mentioned caffeine tracker but in relation toother interventions, whether they be medical or lifestyle drugs such asalcohol, tobacco, etc.), endurance sport coach (e.g., recommending orprescribing the intensity, duration, or profile of arunning/bicycling/swimming workout, or suggesting the abstinence ordelay of a workout, in accordance with a user specified goal such as amarathon, triathlon, or custom goal utilizing data from, for instance,historical exercise activity (e.g., distance run, pace), heart rate,heart rate variability, health/sickness/stress/fever state), weightand/or body composition, blood pressure, blood glucose, food intake orcaloric balance tracker (e.g., notifying the user how many calories hemay consume to maintain or achieve a weight), pedometer, and nail bitingdetector. In some cases, the apps may rely solely on the processingpower and sensors of the present disclosure. In other cases, the appsmay fuse or merely display information from an external device or set ofexternal devices including, but not limited to, a heart rate strap, GPSdistance tracker, body composition scale, blood pressure monitor, bloodglucose monitor, watch, smart watch, mobile communication device such asa smart phone or tablet, or server.

In one embodiment, the biometric monitoring device may control a musicplayer on a secondary device. Aspects of the music player that may becontrolled include, but are not limited to, the volume, selection oftracks and/or playlists, skipping forward or backward, fast forwardingor rewinding of tracks, the tempo of the track, and the music playerequalizer. Control of the music player may be via user input orautomatic based on physiological, environmental, or contextual data. Forexample, a user may be able to select and play a track on their smartphone by selecting the track through a user interface on the biometricmonitoring device. In another example, the biometric monitoring devicemay automatically choose an appropriate track based on the activitylevel of the user (the activity level being calculated from biometricmonitoring device sensor data). This may be used to help motivate a userto maintain a certain activity level. For example, if a user goes on arun and wants to keep their heart rate in a certain range, the biometricmonitoring device may play an upbeat or higher tempo track if theirheart rate is below the range which they are aiming for.

Automated Functions Triggered by User's Activity Sleep Stage TriggeredFunctionality

Sleep stages can be monitored through various biometric signals andmethods disclosed herein, such as heart rate, heart rate variability,body temperature, body motions, ambient light intensity, ambient noiselevel, etc. Such biometrics may be measured using optical sensors,motion sensors (accelerometers, gyroscopic sensors, etc.), microphones,and thermometers, for example, as well as other sensors discussedherein.

The biometric monitoring device may have a communication module as well,including, but not limited to, Wi-Fi (802.xx), Bluetooth (Classic, lowpower), or NFC. Once the sleep stages are estimated, the sleep stagesmay be transmitted to a cloud-based system, home server, or main controlunit that is connected to communication-enabled appliances (with Wi-Fi,Bluetooth, or NFC) wirelessly. Alternatively, the biometric monitoringdevice may communicate directly with the communication-enabledappliances. Such communication-enabled appliances may include, forexample, kitchen appliances such as microwaves, ovens, coffeegrinders/makers, toasters, etc.

Once the sleep stages indicate that it is close the time for the user towake up, the biometric monitoring device may send out a trigger to theappliances that the user has indicated should be operated automatically.For example, the coffee grinder and maker may be caused to start makingcoffee, and the toaster may be caused to start warming up bread. Themicrowave oven may be caused to start cooking oatmeal or eggs as well,and electric kettle to start boiling water. So long as the ingredientsare appropriately prepared, this automated signal may triggerbreakfast-cooking.

Alertness Detection

Alertness, e.g., a low alertness may correlate with a person beingdrowsy, may also be detected from the biometrics listed above, and maybe used to trigger an appliance such as a coffee maker to start brewingcoffee automatically.

Hydration

The portable biometric monitoring device in combination with an activitylevel tracker may submit the user's activity level to a cloud-basedsystem, home server, main control unit, or appliances directly. This maytrigger some actions of the appliances, especially related to hydration,such as starting the ice cube maker of a refrigerator, or loweringoperating temperature of a water purifier.

Power Saving

Many appliances typically operate in a low-power idle state thatconsumes power. Using aggregated information of the user's biometricsignals, communication-enabled appliances may be caused to go into asuper-low power mode. For example, a water dispenser at home may shutitself down into a super-low-power mode when the user is asleep or outfor work, and may start cooling/heating water once the user's activityat home is expected.

Restaurant Recommendation System Based on Location and Activity

Aggregation of real-time biometric signals and location information maybe used to create an educated-guess on one or multiple users' needs fora given time, e.g., ionized drink. Combining this guessed need withhistorical user data on the user's activity levels, activity types,activity time, and activity durations, as well as food intake datalogged by the users, an app on a smart phone and/or smart watch mayrecommend a restaurant that would meet the user's life-style and currentneed.

For example, a user who just finished a six mile circuit may launch thisapp. The app may know that this person maintained a high activity levelfor the past hour, and thus determine that the person may be dehydrated.From the historical user data, the app may also know, for example, thatthe user's diet is heavy on vegetables but low in sugar. With anoptimization algorithm that considers the user's current location, priceranges, and other factors mentioned above, the app may recommend arestaurant that offers smoothies, for example.

Swim Tracking

In some embodiments of a biometric tracking device, the biometrictracking may include a swimming algorithm that may utilize data from oneor more motion sensors, altitude sensors (e.g., such as a barometricpressure sensor), orientation sensors (e.g., magnetometer), locationservice sensor (e.g., GPS, wireless triangulation), and/or temperaturesensors. The sensors may be embedded in a single device mounted to, forinstance, the wrist. In other embodiments, extra sensor devices may beattached to the swimmer's forehead, back of the head, goggles, back,hip, shoulder, thighs, legs, and/or feet.

Three potential functional components of swimming exercise analysis areas follows:

Stroke count detection—provides stroke counts per lap, where a lap isdefined to be a one-way traverse from one end of the pool to theopposite end.

Stroke type classification—describes the swimming stroke type of theuser (e.g., crawl stroke, breast stroke, back stroke, butterfly stroke,side stroke, kicking without strokes, body streamline, etc.) and can beany or a combination of:

a. Classification of each stroke that a user takesb. Classification of the predominant stroke type used per complete lap.c. Classification of stroke type used per fractional lap (e.g. half alap of freestyle, half a lap of breast stroke)

Lap count—counts the laps traversed by the user. One method ofdetermining a lap is by detecting when the user turns in a pool.

Turning is defined to be a 180 degree change in heading direction. As aturn is detected, start and end of a lap may be inferred. Taking a break(no motion for a certain period of time) at a point in the pool(typically at one end or the other) before starting to swim again isalso considered a turn as long as the following heading direction isopposite the heading prior to the break.

In some embodiments, these functional components may be combined in amultitude of ways.

Algorithm Structure

The three functional components of the swimming exercise analysis may beperformed sequentially, in parallel, or in hybrid order (a combinationof some sequential blocks and some parallel blocks).

Sequential Approach (See FIG. 15A)

In one embodiment, raw and/or pre-processed sensor signals may first beanalyzed by a stroke detector algorithm. The stroke detector algorithmmay use temporal peaks (local maxima and/or local minima) in a motionsensor (e.g., accelerometer, gyroscope) as an indication that a strokehas been taken. Then one or more heuristic rules may also be applied toremove peaks that do not represent strokes. For example, the magnitudesof the peaks, temporal distance of two adjacent peaks, peak-to-peakamplitude, and/or morphological characteristics of the peaks (e.g.,sharpness) may indicate that certain peaks do not represent strokes.When sensors provide more than one dimensional data, e.g., such as3-axis accelerometers, or 3 axis motion sensors+altimeter (totaling4-axis data), timings and relevant sizes of peaks in all axes may betaken into account to determine whether or not the peaks in one or moreof the axes are generated by a stroke or not.

If a single peak representing a stroke or group of peaks from multipledata axes representing strokes are observed, features may be extractedfrom a segment of data that are obtained from the time between when theprevious peak is detected and when the current peak is detected.Features include, but are not limited to, maximum and minimum values,number of ripples in the segment, powers measured in various metrics,e.g., L1 power and L2 power, standard deviation, mean, etc. Theextracted features may then be put through a machine learning systemwhere the system coefficients are computed off-line (supervisedlearning) or are adapted as the user uses the biometric monitoringdevice (unsupervised learning). The machine learning system may thenreturn a stroke classification for each detected stroke.

The turn-detector algorithm may search for sudden changes in motion bycalculating derivatives, moving average, and/or using high-passfiltering on the signals of the sensors (the sensors including, but notlimited to, those listed in this disclosure). Principal ComponentAnalysis (PCA) can also and/or alternatively be performed on thesignal(s). If one principle component is different from thesub-sequential one, then it may be determined that a turn occurred.Whole or partial coefficients of a transform, such as the Fast FourierTransform (FFT) may be used as features as well. Parametric models suchas Autoregressive (AR) models may also be used. Time-varying modelparameters may then be estimated using Linear Prediction Analysis (LPA),Least Mean Squares filtering (LMS), Recursive Least Squares filtering(RLS), and/or Kalman filtering. Estimated model parameters are thencompared to determine if there is an abrupt change in their values.

In one embodiment, the skill level and/or swimming styles (e.g., speed)of the swimmer may be inferred from sensor data, and then used in turndetection. For example, advanced swimmers typically have more powerfulstrokes (i.e., large accelerometer peak magnitudes) and take fewerstrokes to complete a lap. Therefore, metrics that estimate theswimmer's skill level or characteristics may be used in a turn detectionalgorithm. These metrics may include, but are not limited to averagedmotion signals, or integrated motion signals in particular armmovements, estimated heading speed, and detected patterns of an advancedswimmer in motion signals. The swimmer's skill level or othercharacteristics may also be determined through user input. For example,the user may input that they are an advanced, intermediate, or beginnerswimmer.

One or many (combined) features from these analyses may be used todetect if a given data sample, and/or neighboring data samples, havecharacteristics of a turn. To obtain the optimal combination of thefeatures and decision boundary, one can utilize machine learningtechniques such as logistic regression, decision tree, neural nets, etc.

In some embodiments, if a turn is detected, the swimming data accruedsince the previous turn may be summarized, such as the number ofstrokes, stroke type for each stroke and for the lap, split time, etc.If no turn is detected, the stroke counter and type may be updated.Unless the user quits swimming, the algorithm may go back to strokecount detection.

Parallel Approach (See FIG. 158)

In the parallel approach, some or all of the three functional componentsmay be executed in parallel. For example, stroke-type detection and turndetection may be performed jointly, while stroke count detection is runindependently.

In such embodiments, two functional components, stroke-type and turndetection, may be implemented in a single algorithm that simultaneouslydetects stroke-types and turns. For example, a classifier of swimmingstroke types, e.g., movement analysis that detects free style strokes,breast stroke strokes, back strokes, butterfly strokes, and of turntypes (e.g. tumble turn, flip turn, two hand touch) may return adetected type of stroke or a type of detected turn. During thedetection, temporal as well as spectral features may be extracted. Amoving window may first be applied to multiple axes of data. Statisticsof this windowed segment may then be computed, namely, maximum andminimum value, number of ripples in the segment, powers measured invarious metrics (e.g., L1 power and L2 power, standard deviation, mean).Independent component analysis (ICA) and/or principal component analysis(PCA) can be applied as well to find any hidden signals that betterrepresent turn-type and stroke-type characteristics. Temporal featuresmay then be computed from this (potentially improved) signalrepresentation. For temporal features, various nonparametric filteringschemes, low-pass filtering, band-pass filtering, high-pass filtering,may be applied to enhance desired signal characteristics.

Spectral analysis such as FFT, wavelet transform, Hilbert transform,etc., may be applied to this windowed segment as well. Whole or partialtransform coefficients may be chosen as features. Parametric models suchas AR, moving average (MA), or ARMA (autoregressive and moving average)models may be used, and the parameters of such a model may be found viaautocorrelation and/or partial autocorrelation, or LPA, LMS, RLS, orKalman filter. The entire or part of estimated coefficients may be usedas features.

Different lengths of moving average windows may be run in parallel, andprovide features listed above, and the whole or part of the features maybe utilized as features as well.

Machine-learned coefficients (supervised learning) may then be appliedto these extracted features. One or more machine learning techniques,namely multiple layers of binomial linear discriminant analysis (e.g.,logistic regression), multinomial logistic regression, neural net,decision tree/forest, or support vector machine, can be trained, andthen used.

As the window of interest moves, the features may be extracted and thesenewly-extracted features will return either a stroke type or detectedturn via a machine learning system.

The stroke detector algorithm may run in parallel independent of stroketype and turn detection. Temporal peaks of raw or pre-filtered sensorsignals may be detected and chosen by heuristic rules.

At the summarizing stage (the stage where metrics regarding the swim maybe determined, displayed, and/or stored) of the algorithm,post-processing may be applied to the sequence of stroke type and turndetections. If a turn is confirmed with certain confidence, the swimmingmetric data from the previous turn may be summarized along with strokecounts detected. If no turn is confirmed, the moving average window mayproceed. Until the user stops swimming, the algorithm may continue toupdate swimming metrics regarding the exercise of the user, including,but not limited to, a total number of turns, total number of laps, totalnumber of strokes, average strokes per lap, number of strokes in thelast lap, the change in number of strokes per lap, etc.

Hybrid Approach (See FIGS. 15C and 15D)

In a hybrid approach, the stroke type and stroke count detection may berun in parallel, followed by turn detection.

Stroke-type detection may return a stroke type via machine learnedcoefficients. A first moving window may take segments of sensor signals.Then features, either entire features or a subset of the moving windowfeatures listed in herein, may be extracted. The machine learningcoefficients, trained off-line, may then be applied to the features todetermine which stroke-type generated the given segments of sensorsignals.

Along with stroke type detection, stroke count detection may be runsimultaneously.

Once the stroke type and counts are detected, turn detection may beperformed with either the entire feature or a subset of the featureslisted.

If a turn is detected, completion of a lap may be recorded in theswimming summary metrics of the user. A post process may be applied todetected stroke types to determine the most prominent stroke type forthe completed lap. Then the algorithm may move to the stroke-type andcount detection stages unless the user stops swimming. If no turn isdetected, the algorithm may continue updating stroke types and counts ofthe current lap until a turn is detected.

Blood Glucose Level and Heart Rate

Biometric monitoring devices that continuously measure biometric signalsmay provide meaningful information on preconditions of, progresstowards, and recoveries from diseases. Such biometric monitoring devicesmay have sensors and run algorithms accordingly to measure and calculatebiometric signals such as heart rate, heart rate variability, stepstaken, calories burned, distance traveled, weight and body fat, activityintensity, activity duration and frequency, etc. In addition to themeasured biometric signals, food intake logs provided by users may beused.

In one embodiment, a biometric monitoring device may observe heart rateand its changes over time, especially before and after a food intakeevent or events. It is known that heart rate is affected by blood sugarlevel, whereas it is well known that high blood sugar level is apre-diabetic condition. Thus, mathematical models that describe therelation between time elapsed (after food intake) and blood sugar levelmay be found via statistical regression, where data are collected fromnormal, pre-diabetic, and diabetic individuals to provide respectivemathematical models. With the mathematical models, one may predictwhether an individual with specific heart rate patterns is healthy,pre-diabetic, or diabetic.

Knowing that many heart failures are associated with pre-diabetic ordiabetic conditions, it is possible to further inform users of biometricmonitoring devices with possible heart failures, e.g., coronary heartdisease, cerebrovascular disease and peripheral vascular disease etc.,of such risks based on their biometric data.

Users' activity intensity, type, duration, and frequency may also betaken into account, when developing the mathematical models, as anargument that controls “probability” of the disease onset, usingrecommended exercise guidelines such as guidelines provided by AmericanHeart Association (http://www.heart.org/). Many guidelines on nutritionand weight management are also available in academia and to the generalpublic to prevent cardiovascular and diabetic disease. Such guidelinesmay be incorporated into the mathematical models with the user dataaccumulated over time, such as ingredients of the food that the usersconsumed, and weight and body fat trends.

If users have set their family members as their friends on a socialnetwork site, which stores and displays biometric data, the likelihoodof the family members getting a disease may also be analyzed and theusers informed of the results.

In addition to informing users regarding a potential development ofdisease, recommended life-style including exercise regime and recipeswith healthier ingredients and methods of preparation may be provided tothe users.

Unification of Grocery Shopping, Cooking, and Food Logging GroceryOrganizing and Recipe Recognition System

Receipts from grocery shopping may contain copious information,especially regarding an individual's eating habits. A novel system thatcombines information from grocery store receipts with an individual'sbiometric data, as collected by a biometric monitoring device, forexample, is presented here. The system may collect and analyze data(information) regarding an individual, and may then recommend optionsthat may change the individual's life-style so as to improve theirhealth. The implementation of this system may involve cloud computing,hardware platform development for sensing and interface, andmobile/website site development.

In one embodiment, when a user checks out at a grocery store, the listof groceries (as obtained from the receipt or, for example, from anemail receipt or invoice) may be transmitted automatically to a remotedatabase (e.g., a cloud server), that may also store the user'sbiometric data. When the user gets home and organizes items in theirrefrigerator and/or pantry, an app on their smart phone/watch mayrecommend which items in the pantry or refrigerator to throw away basedon historical data on food items (e.g., if food items are expired orlikely to have gone bad). Alerts indicating when food has expired orthat it should be consumed in the near future to avoid spoilage may beautomatically sent to the user independently of such activity. Forexample, these alerts may be sent out to the user whenever a certainthreshold has been met (e.g., in two days the milk will expire). Thealerts may also be sent to the user through means other than through asmart phone/watch. For example, the alerts may be presented to the userthrough a web interface, through email, through an alert on a laptopcomputer, on a tablet computer, desktop computer, or any otherelectronic device which is in direct or indirect communication with thecomputer which maintains and/or analyzes the database of food

Using the updated list of food items, and based on the user's historicalfood consumption data, the app may recommend recipes to the user. In oneembodiment, preference may be given to recipes that use the items whatshould be eaten first (e.g., before they expire, go bad, or become lessfresh faster than other ingredients). To recommend the optimal recipethat is nutritionally balanced, correctly portioned, and tailored to theuser's activity, the app may also analyze the user's activity data aswell. For example, if the user lifted weights in the morning,high-protein meals may be recommended. In another example, if the userwas not very active, the size of the recipe may be decreased to lowerthe number of calories that the final meal contains.

Note that these strategies may be applied to multiple users that eithershare the same food and/or meals. For example, a combined food databasemay be created for a household so that if one member of the house goteggs and another member of the house got milk from the grocery storethat both eggs and milk would be represented in the food database.Similarly, the nutritional preferences (e.g., vegetarian, allergic tocertain foods, etc.), activity, basal metabolic rate, and total calorieburn may be used to form a recommendation on what food/recipe to prepareand/or purchase.

Biometric signals including, but not limited to, heart rate and heartrate variability may provide indications of pre-conditions of diseases.This information may be used to recommend that the user purchase,consume, and/or prepare particular foods so as to reduce their risk ofthe disease(s) for which they have the pre-conditions. For example, if auser has a precondition for cardiac problems, it may be recommended thatthey purchase more vegetables, consume less fatty foods, and preparefood in methods which require less oil (e.g., not deep frying).

Control “Smart Appliance”

In another embodiment, various appliances may all be Wi-Fi enabled, andmay communicate with servers. Since the app (which may be connected tothe appliances via, for example, the cloud or the Internet) may knowwhich food items the refrigerator contains, the app may communicate withthe refrigerator to lower or raise the temperature of the refrigeratordepending on the food items. For example, if many of the food items aremore sensitive to cold, such as vegetables, the refrigerator may beinstructed to raise the temperature. The app may also directlycommunicate with the refrigerator as well via Bluetooth, BTLE, or NFC.

Food Logging

The app may also provide items to log in as the user's food based on agrocery shopping list (which may, for example, be a list maintainedwithin the app) and food recipes that the app recommended. In case ofprecooked meals (e.g., frozen dinner) or produce that does not requireany further processing before being eaten, the user may simply inputtheir serving size (or in the case that the user eats the whole meal,the user may not need to enter a serving size), and then the foodlogging will be completed. Since the grocery list or receipt providesthe exact brand and maker of certain foods, more accurate nutritionalinformation may be logged into the user's account.

When a user logs a food item that is cooked by following a recipesuggested by the app, the app may calculate nutritional information fromthe ingredients and cooking procedure. This may provide more accurateestimate of calorie intake than a simple categorization of the endproduct/meal, since many recipes exist to prepare a particular type offood, e.g., meatballs for pasta may be made with beef, turkey, pork,etc., and may include varying degrees of carbohydrates.

Sport Metric Acquisition Using a Sensor Device

In some embodiments, a sensor may be mounted on a racket, e.g., tennisracket, to help to measure the different strokes of the player. This maybe applicable to most, if not all, racket sports including, but notlimited to, tennis, racquetball, squash, table tennis, badminton,lacrosse, etc., as well as sports played with a bat like baseball,softball, cricket, etc. Similar techniques may also be used to measuredifferent aspects of golf. Such a device can be mounted on the base ofthe racket, on the handle or on the shock absorber typically mounted onthe strings. This device may have various sensors like an accelerometer,gyroscope, magnetometer, strain sensor, and/or microphone. The data fromthese sensors may either be stored locally or transmitted wirelessly toa host system on a smartphone or other wireless receiver.

In some embodiments of a biometric monitoring device, a wrist mountedbiometric monitoring device including an accelerometer, gyroscope,magnetometer, microphone, etc. may perform similar analysis of theuser's game or motions. This biometric monitoring device may take theform of a watch or other band worn on the user's wrist. Racket- orbat-mounted sensors that measure or detect the moment of impact betweenthe bat or racket and the ball and wirelessly transmit such data to thewrist-mounted biometric monitoring device may be used to improveaccuracy of such algorithms by accurately measuring the time of impactwith the ball.

Both wrist and racket-/bat-mounted devices may help measure differentaspects of the user's game including, but not limited to, stroke-type(forehand, backhand, serve, slice, etc.), number of forehands, number ofbackhands, ball spin direction, topspin, service percentage, angularvelocity of racket head, backswing, shot power, shot consistency, etc.The microphone or the strain sensor may be used in addition to theaccelerometer to identify the moment at which the ball impacts theracket/bat. In cricket and baseball, such a device may measure thebackswing, the angular velocity of the bat at the time of impact, thenumber of shots on the off-side vs. leg-side (cricket). It may alsomeasure the number of swings and misses and the number of defensive vs.offensive strokes. Such a device may also have a wireless transmitter totransmit such statistics in real time to a scoreboard or to individualdevices held by spectators.

The wrist- or racket-mounted device may have a small number of buttons(e.g., two) that may be used by the player to indicate when a volley iswon or when an unforced error occurs. This will allow the algorithm tocalculate the fraction of winners and unforced errors that are forehandsvs. backhands. The algorithm may also keep track of the number of acesvs. double-faults in tennis. If both players use such a system, thesystem may also automatically keep track of the score.

Bicycle Handlebar Based ECG

In some embodiments of biometric monitoring devices, a user's heart ratemay be monitored using an electrode in contact with the left hand and anelectrode in contact with the right hand (an ECG heart ratemeasurement). As riding a bicycle requires the user to make hand contactwith either side of the handlebars, this particular activity is wellsuited to tracking user heart rate using ECG techniques. By embeddingelectrodes in the handlebars or handlebar grips or tape, the user'sheart rate may be measured whenever the user is holding the handlebars.For bicycles that have grips (as opposed to using handlebar tape),electrodes may be incorporated into a special grip that may be used toreplace the existing grips, e.g., the factory-installed grips, which aretypically non-conductive. The left and right grips may be electricallyconnected to electronics that measure the ECG signal, using a wire, forexample. In the case that the handlebars themselves are conductive, thehandlebars may be used to electrically connect one of the grips to theelectronics that measure the ECG signal. The electronics that measurethe ECG signal may be incorporated into one or both of the grips.Alternatively, the electronics that measure the ECG signal may belocated in a separate housing. In one embodiment, this separate housingmay be mounted on the bicycle handlebar or stem. It may have functionsand sensors that typical bicycle computers have (e.g., speed sensor,cadence sensor, GPS sensor). It may also have atypical sensors such as awind speed sensor, GSR sensor(s), and accelerometer sensor (potentiallyalso incorporated into the handlebars). This embodiment may usetechniques described in this disclosure to calculate activity metricsincluding, but not limited to, calorie burn, and transmit these metricsto secondary and tertiary device(s) (e.g. smartphones and servers).

Electrodes for the ECG may be incorporated into parts of the bike oraccessories other than into grip tape and handlebar grips such as intogloves, brake hoods, brake levers, or the handlebars themselves. Theseelectrodes or additional electrodes may be used to measure GSR, body fatand hydration in addition to, or in alternative to, heart rate. In oneexample, the user's heart rate may be measured using conductive threads(used as ECG electrodes) sewn into grip tape installed on the handlebar.The grip tape electrodes may be connected to a central bike computerunit that contains electronics to measure GSR, hydration, and/or heartrate. The biometric monitoring device may display this information on adisplay. If the user's hydration or heart rate exceeds a certainthreshold, the user may be alerted to drink more, drink less, increaseintensity or decrease intensity. In the case that the bike computermeasures only one or two of GSR, hydration or heart rate, algorithms maybe used to estimate metrics which that cannot be measured directly. Forexample, if the biometric monitoring device can only measure heart rateand duration of exercise, a combination of heart rate and duration ofexercise may be used to estimate hydration and alert the user when theyshould drink. Similarly, heart rate and exercise duration may be used toalert the user when they should eat or drink something other than water(e.g., a sports drink).

Indirect Metric Estimation

Bicycle computers typically measure a variety of metrics including, butnot limited to, speed, cadence, power, and wind speed. In the case thatthe portable monitoring device does not measure these metrics or is notin communication with devices which may be able to supply these metrics,these and other metrics may be inferred using the sensors that theportable biometric monitoring device does have. In one embodiment, theportable biometric monitoring device may measure heart rate. It may usethis measurement to infer/estimate the amount of power that the user isoutputting. Other metrics such as the user's age, height, and weight mayhelp inform the power measurement. Additional sensor data such asGPS-measured speed, altitude gain/descent, bicycle attitude (so as themeasure the incline or decline of a slope), and accelerometer signalsmay be used to further inform the power estimate. In one embodiment, anapproximately linear relationship between heart rate and power outputmay be used to calculate the user's power output.

In one embodiment, a calibration phase may occur where the user takesdata from the portable biometric monitoring device and a secondarydevice that may be used during calibration as a baseline but not be usedat a later time (e.g., a power meter). This may allow a relationshipbetween sensor data measured by the portable monitoring device andsensor data measured by the secondary device data to be determined. Thisrelationship may then be used when the secondary device is not presentto calculate estimated values of data that is explicitly provided by thesecondary device but not by the biometric monitoring device.

Activity Based Automatic Scheduling

In one embodiment, the day's travel requirements (to work, from work,between meetings) may be scheduled for the user based on the informationin their calendar (or emails or text messages etc.), with the aim ofmeeting daily activity goal(s) or long term activity goal(s). The user'shistorical data may be used to help plan both meeting the goal(s) andalso the transit time required. This feature may be combined withfriends or colleagues. The scheduling may be done such that a user maymeet a friend along the way as they walk to work, or meet a colleague onthe way to a meeting (the user might need to set a rendezvous point,though). If there is real-time communication between biometricmonitoring devices of the user and the user's friend, the user may bedirected to walk a longer route if data from the friend's biometricmonitoring device indicates that their friend is running late.

In another embodiment, walking/running/fitness routes may be suggestedto the user based (in whole or in part) on their proximity to the user.The data for such recommendations could also or additionally be based onGPS info from other users. If there is real-time communication, the usermay be directed to a busy route or a quiet route as preferred. Knowingheart rate and basic fitness information about other users may allow thesystem to suggest a route to match a user's fitness level and thedesired exercise/exertion level. Again this information may be used forplanning/guiding a user to longer term activity/fitness goals.

Location/Context Sensing and Applications

Through one or more methods, embodiments of the biometric monitoringdevices disclosed herein may have sensors that can determine or estimatethe location and or context (e.g. in a bus, at home, in a car) of thebiometric monitoring device. Purpose-built location sensors such as GPS,GLONASS, or other GNSS (Global Navigation Satellite System) sensors maybe used. Alternatively, location may be inferred, estimated or guessedusing less precise sensors. In some embodiments in which it is difficultto know the user's location, user input may aid in the determination oftheir location and or context. For example, if sensor data makes itdifficult to determine if a user was in a car or a bus, the biometricmonitoring device or a portable communication device in communicationwith the biometric monitoring device or a cloud server which is incommunication with the biometric monitoring device may present a queryto the user asking them if they took the bus today or took a car.Similar queries may occur for locations other than vehicular contexts.For example, if sensor data indicate that the user completed a vigorousworkout, but there is no location data that indicates that the user wentto a gym, the user may be asked if they went to the gym today.

Vehicular Transportation Detection

In some embodiments, sensors of the biometric monitoring device and/or aportable electronic device in communication with the biometricmonitoring device and/or a server which communicates with the biometricmonitoring device may be used to determine what type of vehicle (if any)the user is, or was, in. Note that in the embodiments below, a sensor inone or more biometric monitoring devices and/or portable electronicdevices may be used to sense the relevant signal. Also note that whilespecific network protocols such as WiFi or Bluetooth may be used in thefollowing descriptions, one or more alternative protocols such as RFID,NFC, or cellular telephony may also be used.

In one embodiment, the detection of a Bluetooth device associated with avehicle may be used to infer that the user is in a vehicle. For example,a user may have a car that has a Bluetooth multimedia system. When theuser gets close enough to their car for a long enough period of time,the sensor device may recognize the Bluetooth identification of themultimedia system and assume that the user is in the car. Data fromother sensors may be used to corroborate the assumption that the user isin the vehicle. Examples of data or signals from other sensors that maybe used to confirm that the user is in a car include a GPS speedmeasurement that is higher than 30 mph and accelerometer signals thatare characteristic of being in a car. Information intrinsic to theBluetooth ID may be used to determine that it is a Wi-Fi router of avehicle or type of vehicle. For example, the Bluetooth ID of a router ina car may be “Audi In-Car Multimedia.” The keyword “Audi” or “Car” maybe used to guess that the router is associated with a vehicle type of“car.” Alternatively, a database of Bluetooth ID's and their associatedvehicles may be used.

In one embodiment, a database of Bluetooth ID's and their associatedvehicles may be created or updated by the user of a biometric monitoringdevice or through portable communication device data. This may be donewith and/or without the aid of user input. In one embodiment if abiometric monitoring device can determine whether or not it is in avehicle, vehicle type, or specific vehicle without the use of BluetoothID, and it encounters a Bluetooth ID that moves with the vehicle, it maysend the Bluetooth ID and information regarding the vehicle to a centraldatabase to be catalogued as a Bluetooth ID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is aBluetooth ID that was encountered during or close to the time that theuser indicated they were in the vehicle, the Bluetooth ID and vehicleinformation may be sent to a central database and associated with oneanother.

In another embodiment, the detection of a Wi-Fi device associated with avehicle may be used to infer that the user is in that vehicle or type ofvehicle. Some trains, buses, airplanes, cars, and other vehicles haveWi-Fi routers in them. The SSID of the router may be detected and usedto infer or aid an inference that a user is in a specific vehicle ortype of vehicle.

In one embodiment, a database of SSID's and their associated vehiclesmay be created or updated with the user of a biometric monitoring deviceor through portable communication device data. This may be done withand/or without the aid of user input. In one embodiment, if a biometricmonitoring device can determine whether or not it is in a vehicle,vehicle type, or specific vehicle without the use of an SSID, and itencounters an SSID that moves with the vehicle, the biometric monitoringdevice may send the SSID and information regarding the vehicle to acentral database to be catalogued as an SSID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is an SSIDthat was encountered during or close to the time that the user indicatedthey were in the vehicle, the SSID and vehicle information may be sentto a central database and associated with one another.

In another embodiment of a biometric monitoring device, location sensorsmay be used to determine the track of a user. This track may then becompared to a database of routes for different modes of transit. Modesof transit may include, but are not limited to walking, running, biking,driving, taking a bus, taking a train, taking a tram, taking the subway,and/or motorcycling. If the user's track corresponds well with a routeof a specific mode of transit, it may be assumed that the user used thatmode of transit for the period of time that it took them to traverse theroute. Note that the speed with which the route or sections of the routewere completed may improve the guess of the mode of transit. Forexample, a bus and a car may both be able to take the same route, butthe additional stopping of the bus at bus stops may allow the device todetermine that the user was taking a bus rather than a car. Similarly,the discrimination between biking and driving a route may be aided bythe typical difference of speed between the two. This difference inspeed may also depend on the time of day. For example, some routes maybe slower by car during rush hour.

In another embodiment, a biometric monitoring device may be able todetect that the user is in or near a vehicle based on measurements ofthe magnetic field of vehicle. In some embodiments, the magnetic fieldsignature of a location typically associated with the vehicle (e.g.,train station, subway station, bus stop, car garage) may also be used toinfer that the user is currently in, will be, or has been in a vehicle.The magnetic field signature may be time invariant or time varying.

If it is determined that the user was indeed in a vehicle for a periodof time, other metrics about the user may be modified to reflect such astatus. In the case that the biometric monitoring device and/or portableelectronic device can measure activity metrics such as steps taken,distance walked or run, altitude climbed, and/or calories burned, thesemetrics may be modified based on information about vehicular travel. Ifany steps taken or altitude climbed were incorrectly logged during thetime that the user is in a vehicle, they may be removed from the log ofmetrics about the user. Metrics derived from the incorrectly loggedsteps taken or altitude climbed such as distance travelled and caloriesburned may also be removed from the log of metrics about the user. Inthe case that it can be determined in real-time or near real-timewhether or not the user is in a vehicle, the sensors detecting metricswhich should not be measured while in a vehicle (e.g. steps taken,stairs climbed) may be turned off or algorithms which are used tomeasure these metrics may be turned off to prevent incorrectly loggedmetrics (as well to save power). Note that metrics regarding vehicle usesuch as type of vehicle taken, when it was taken, which route was taken,and how long the trip took may be recorded and used later to present theuser with this data and/or to correct other activity and physiologicalmetrics about the user.

Location Sensing Using Bluetooth

Methods similar to those described above may also be used by a biometricmonitoring device to determine when the user comes into proximity ofstatic locations. In one embodiment, Bluetooth ID's from computers(e.g., tablet computers) at restaurants or stores may be used todetermine the user's location. In another embodiment, semi-fixedBluetooth ID's from portable communication devices (e.g., smartphones)may be used to determine a user's location. In the case of semi-fixedBluetooth ID sources, multiple Bluetooth ID's may be need to reach anacceptable level of confidence of the location of the user. For example,a database of Bluetooth ID's of the coworkers of a user may be created.If the user is within range of several of these Bluetooth ID's duringtypical working hours, it may be assumed that the user is at work. Thedetection of other Bluetooth ID's may also be used to record when twousers meet up. For example, it may be determined that a user went for arun with another user by analyzing pedometer data and Bluetooth ID's.Similar such concepts are discussed in further detail in U.S.Provisional Patent Application No. 61/948,468, filed Mar. 5, 2014, andpreviously incorporated by reference with regard to such concepts.

Uncertainty Metric for GPS Based on Location

When fusing sensor signals with GPS signal to estimate informativebiometrics, such as steps, live pace, speed, or trajectory of trips,quality of the GPS signal is often very informative. However, GPS signalquality is known to be time-varying, and one of the factors that affectsthe signal quality is environmental surroundings.

Location information may be used to estimate GPS signal quality. Aserver may store a map of area types, where the area types arepre-determined by number and kind of objects that deteriorate GPSsignals. The types may, for example, be: large building area, smallbuilding area, open area, side-by-water area, and forested area. Thesearea types are then queried when GPS sensor gets turned on with its veryfirst few location estimates, which are expected to be rough andinaccurate. With the rough GPS estimates of the location, possible typesof areas may be returned, and these area types may then be taken intoaccount in the calculation of the GPS signal quality and reliability.

For example, if a user is in or near an urban canyon (an area surroundby tall buildings) such as downtown San Francisco, a low certainty maybe associated with any GNSS location measurements. This certainty may beused later by algorithms that attempt to determine the user's track,speed, and/or elevation based on, at least in part, GPS data.

In one embodiment, a database of location and GPS signal quality may becreated automatically using data from one or more GNSS sensors. This maybe automatically performed by comparing the GNSS tracks with a map ofstreets and seeing when the GNSS sensors show characteristics of a usertravelling along a street (e.g., having a speed of 10 mph or higher),but their track is not located on a road. The database of GPS certaintybased on approximate location may also be inferred from maps showingwhere there are tall buildings, canyons, or dense forests.

Location Sensing Using Vehicular GNSS and/or Dead Reckoning

Many vehicles have integrated GNSS navigation systems. Users of vehiclesthat don't have integrated GNSS navigations systems often buy a GNSSnavigation system for their car that is typically mountednon-permanently in the driver's field of view. In one embodiment, aportable biometric monitoring device may be able to communicate with thevehicle's GNSS system. In the case where the portable biometricmonitoring device is also used to track location, it may receivelocation information from the vehicle GNSS. This may enable thebiometric monitoring device to turn off its own GNSS sensor (in the casethat it has one), therefore reducing its power consumption.

In addition to GNSS location detection, a vehicle may be able totransmit data about its steering wheel orientation and/or itsorientation with respect to the earth's magnetic field in addition toits speed as measured using the tire size and tire rotational velocity.This information may be used to perform dead-reckoning to determine atrack and/or location in the case that the vehicle does not have a GNSSsystem or the vehicle's GNSS system cannot get a reliable locationmeasurement. Dead-reckoning location information may supplement GNSSsensor data from the biometric monitoring device. For example, thebiometric monitoring device may reduce the frequency with which itsamples GNSS data and fill in the gap between GNSS location data withlocations determined through dead reckoning.

Step Counter Data Fusion with Satellite-Based Location Determination

In some implementations of a biometric monitoring device, data fromvarious different sensors may be fused together to provide new insightsas to activities of the wearer of the biometric monitoring device. Forexample, data from an altimeter in the biometric monitoring device maybe combined with step count data obtained by performing peak detectionanalysis on accelerometer data from an accelerometer of the biometricmonitoring device to determine when the wearer of the biometricmonitoring device is, for example, climbing stairs or walking uphill (asopposed to riding an elevator or an escalator or walking across flatground).

In another example of sensor data fusion, data from a step counter suchas that discussed above may be combined with distance measurementsderived from GPS data to provide a refined estimate of total distancetraveled within a given window. For example, GPS-based distance or speeddata may be combined with step-counter-based distance or speed (usingsteps taken multiplied by stride length, for example) using a Kalmanfilter in order to obtain a refined distance estimate that may be moreaccurate than either the GPS-based distance or speed measurement or thestep-counter-based distance or speed measurement alone. In anotherimplementation, a GPS-based distance measurement may be filtered using asmoothing constant that is a function of the step rate as measured by,for example, an accelerometer. Such implementations are discussedfurther in U.S. Provisional Patent Application No. 61/973,614, filedApr. 1, 2014, which was previously incorporated herein by reference inthe “Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed atdistance or speed estimation refinement using data from satellite-basedlocation systems and step count sensors.

Biometric and Environmental/Exercise Performance Correlation

Some embodiments of portable monitoring devices described herein maydetect a variety of data including biometric data, environmental data,and activity data. All of this data may be analyzed or presented to auser to facilitate analysis of or correlation between two or more typesof data. In one embodiment, a user's heart rate may be correlated to carspeed, biking speed, running speed, swimming speed or walking speed. Forexample, the user may be presented with a graph that plots biking speedon the X axis and heart rate on the Y axis. In another example, a user'sheart rate may be correlated to music that they were listening to. Thebiometric monitoring device may receive data regarding what music theuser was listening to through a wireless connection (e.g., Bluetooth) toa car radio. In another embodiment, the biometric monitoring device mayalso function as a music player itself, and therefore can record whichsong was played when.

Weight Lifting Aid

Without the aid of a personal trainer or partner, it may be difficult todo a weight-lifting routine properly. A portable biometric monitoringdevice may aid a user in completing a weight lifting routine bycommunicating to the user how long they should hold up each weight, howquickly they should lift it, how quickly they should lower it, and howmany repetitions of each lift to perform. The biometric monitoringdevice may measure the user's muscle contractions using one or more EMGsensors or strain sensors. The user's muscle contractions may also beinferred by measuring vibrations of one or more body parts (for exampleusing an accelerometer), sweat (e.g., using a GSR sensor), rotation ofone or multiple body parts (e.g., using a gyroscope), and/or atemperature sensor on one or more body parts. Alternatively, a sensormay be placed on the weight lifting apparatus itself to determine whenthe using is lifting, with how much speed they are lifting or lowering,how long they are lifting for, and how many repetitions of lifts theyhave performed.

In one embodiment, if the biometric monitoring device or weight liftingapparatus detects that the user is approaching their failure limit (whenthe user can no longer support the weight), the weight lifting apparatusmay automatically lift the weight or prevent the weight from beinglowered. In another embodiment, a robot in communication with thebiometric monitoring device or weight lifting apparatus mayautomatically lift the weight or prevent the weight from being lowered.This may allow the user to push themselves to their limit withoutneeding a partner/spotter (to lift the weight in case of failure) andwithout risking injury from dropping the weight.

Glucose Level Monitoring Aid

In some embodiments, a portable biometric monitoring device may beconfigured to aid users who need to monitor their glucose levels (e.g.,diabetics). In one embodiment, the portable biometric monitoring devicemay indirectly infer a user's glucose level or a metric related to theuser's glucose level. Sensors other than those typically used inmonitoring glucose monitoring (using continuous or discrete finger-pricktypes of sensors) may be used in addition to, as an alternative to, oras an aid to the typical glucose monitoring methods. For example, anbiometric monitoring device may alert the user that they should checktheir blood glucose level based on data measured from sensors on thebiometric monitoring device. If the user has performed a certain type ofactivity for a certain amount of time, their blood glucose level islikely to have decreased, and therefore, the biometric monitoring devicemay display an alert, create an auditory alert, or vibrate to alert theuser that their blood glucose may be low and that they should check itusing a typical glucose measurement device (e.g., a finger-prick typeglucose monitor). The biometric monitoring device may allow the user toinput the glucose level that is measured from the glucose meter.Alternatively, the glucose measurement may be automatically transmittedto the biometric monitoring device and/or a third device in direct orindirect communication with the biometric monitoring device (e.g., asmart phone or server). This glucose measurement may be used to informthe algorithm used by the biometric monitoring device to determine whenthe next glucose level alert should be delivered to the user. The usermay also be able to enter what food they ate, are eating, or areplanning to eat into the biometric monitoring device or a device indirect or indirect communication with the biometric monitoring device.This information may also be used to determine when the user should bealerted to check their blood glucose level. Other metrics and sensordata described herein (e.g., heart rate data) may also be used alone orin combination to determine when the user should be alerted to checktheir blood glucose.

In addition to being alerted when glucose levels should be checked, abiometric monitoring device may also display an estimate of the currentglucose level. In another embodiment, data from the biometric monitoringdevice may be used by a secondary device (e.g., a smart phone or server)to estimate the user's glucose level and/or present this data to theuser (e.g., by displaying it on a smartphone, on a webpage, and/or bycommunicating the data through audio).

A biometric monitoring device may also be used to correlate exercise,diet, and other factors to blood glucose level. This may aid users inseeing the positive or negative effects of these factors on their bloodglucose levels. The blood glucose levels with which the activity iscorrelated to may be measured by the user using a different device(e.g., a finger-prick monitor or continuous blood glucose monitor), bythe biometric monitoring device itself, and/or by inferring the bloodglucose level or a metric related to the glucose level using othersensors. In some embodiments of biometric monitoring devices, a user maywear a continuous glucose monitoring device and a biometric monitoringdevice. These two devices may automatically upload data regardingactivities and glucose levels to a third computing device (e.g., aserver). The server may then analyze the data and/or present the data tothe user so that they become more aware of the relationship betweentheir activities and glucose levels. The server may also receive inputon the user's diet (e.g., the user may enter what foods they eat) andcorrelate the diet with glucose levels. By helping the user understandhow diet, exercise, and other factors (e.g., stress) affects their bloodglucose levels, biometric monitoring devices may aid users who havediabetes.

UV Exposure Detection

In one embodiment, the biometric monitoring device may have the abilityto monitor an individual's exposure to UV radiation. UVA and UVB may bemeasured with one or multiple sensors. For example, a photodiode havinga bandpass filter which passes only UVA may detect UVA exposure and aphotodiode having a bandpass filter which passes only UVB may detect UVBexposure. The user's skin pigmentation may also be measured using acamera or reflectometer (light emitter and light detector whichdetermines the efficiency with which light is reflected off the skin).Using UVA, UVB, and skin pigmentation data, the biometric monitoringdevice may provide a user with information regarding the amount of UVexposure they have been subjected to. The biometric monitoring devicemay also provide estimates or alarms regarding over exposure to UV,potential for sunburn, and potential for increasing their risk of skincancer.

Screen Power Saving Using User Presence Sensors

The portable biometric monitoring device may have one or more a displaysto present information to the user. In one embodiment sensors on thebiometric monitoring device may determine the user is using thebiometric monitoring device and/or wearing the biometric monitoringdevice to determine the state of the display. For example, a biometricmonitoring device having a PPG sensor may use the PPG sensor as aproximity sensor to determine when the user is wearing the biometricmonitoring device. If the user is wearing the biometric monitoringdevice, the state of the screen (e.g. a color LCD screen) may be changedto “on” or “standby” from its typical state of being off.

Power Conservation with Respect to Satellite-Based LocationDetermination Systems

In some implementations, certain systems included in a biometricmonitoring device may consume relatively large amounts of power comparedto other systems in the biometric monitoring device. Due to the smallspace constraints of many biometric monitoring devices, this mayseriously affect overall battery charge life for the biometricmonitoring device. For example, in some biometric monitoring devices, asatellite-based location determination system may be included. Each timethe satellite-based location determination system is used to obtain aposition fix using data from the GPS satellite constellation, it usespower drawn from the biometric monitoring device battery. The biometricmonitoring device may be configured to alter the frequency with whichthe satellite-based location determination system obtains a location fixbased on data from one or more sensors of the biometric monitoringdevice. This adaptive location fix frequency functionality may helpconserve power while still allowing the satellite-based locationdetermination system to provide location fixes at useful intervals (whenappropriate).

For example, if a biometric monitoring device has an ambient lightsensor, data from the ambient light sensor may be used to determinewhether the lighting conditions indicate that the biometric monitoringdevice is likely indoors as opposed to outdoors. If indoors, thebiometric monitoring device may cause the location fix frequency to beset to a level that is lower than the location fix frequency that may beused when the lighting conditions appear to indicate that the biometricmonitoring device is outdoors. This has the effect of decreasing thenumber of location fixes that are attempted when the biometricmonitoring device is indoors and thus less likely to obtain a goodlocation fix using a satellite-based location determination system.

In another example, if motion sensors of the biometric monitoring deviceindicate that the wearer of the biometric monitoring device issubstantially stationary, e.g., sleeping or generally not moving morethan a few feet every minute, the location fix frequency of thesatellite-based location determination system may be set to a lowerlevel than if the motion sensors indicate that the wearer of thebiometric monitoring device is in motion, e.g., walking or running fromone location to another, e.g., moving more than a few feet.

In yet another example, the biometric monitoring device may beconfigured to determine if the biometric monitoring device is actuallybeing worn by a person—if not, the biometric monitoring device may setthe location fix frequency to a lower level than if the biometricmonitoring device is actually being worn. Such determinations regardingwhether or not the biometric monitoring device is being worn may bemade, for example, when motion data collected from motion sensors of thebiometric monitoring device indicate that the biometric monitoringdevice is substantially immobile, e.g., not even demonstrating smallmovements experienced by biometric monitoring devices when the wearer issleeping or sedentary, or when data, for example, from a heartbeatwaveform sensor indicates that no heart rate is detected. For opticalheartbeat waveform sensors, if there is little or no change in theamount of light detected by the light detection sensor when the lightsource is turned on and off, this may be indicative of the fact that theheartbeat waveform sensor is not pressed against a person's skin andthat, by inference, the biometric monitoring device is not being worn.Such adaptive satellite-based location determination system fixfrequency concepts are discussed in more detail in U.S. ProvisionalPatent Application No. 61/955,045, filed Mar. 18, 2014, which waspreviously incorporated herein by reference in the “Cross-Reference toRelated Applications” section and which is again hereby incorporated byreference with respect to content directed at power conservation in thecontext of satellite-based location determination systems.

It is to be understood that biometric monitoring devices, in addition toincluding the features discussed below in more detail, may also includeone or more features or functionalities discussed above or discussed inthe various applications incorporated by reference in the abovediscussion. Such implementations are to be understood as being withinthe scope of this disclosure.

There are many concepts and embodiments described and illustratedherein. While certain embodiments, features, attributes, and advantageshave been described and illustrated herein, it should be understood thatmany others, as well as different and/or similar embodiments, features,attributes and advantages are apparent from the description andillustrations. As such, the above embodiments are merely provided by wayof example. They are not intended to be exhaustive or to limit thisdisclosure to the precise forms, techniques, materials and/orconfigurations disclosed. Many modifications and variations are possiblein light of this disclosure. It is to be understood that otherembodiments may be utilized and operational changes may be made withoutdeparting from the scope of the present disclosure. As such, the scopeof the disclosure is not limited solely to the description above becausethe descriptions of the above embodiments have been presented for thepurposes of illustration and description.

Importantly, the present disclosure is neither limited to any singleaspect nor embodiment, nor to any combinations and/or permutations ofsuch aspects and/or embodiments. Moreover, each of the aspects of thepresent disclosure, and/or embodiments thereof, may be employed alone orin combination with one or more of the other aspects and/or embodimentsthereof. For the sake of brevity, many of those permutations andcombinations will not be discussed and/or illustrated separately herein.

What is claimed is:
 1. A method of determining a user's heart rate usinga worn biometric monitoring device comprising a plurality of sensorsincluding a heartbeat waveform sensor and a motion detecting sensor, themethod comprising: (a) collecting concurrent output data from theheartbeat waveform sensor and output data from the motion detectingsensor, wherein the output data from the heart beat waveform sensorprovides information about the user's heart rate and wherein the outputdata from the motion detecting sensor provides information about theuser's periodic physical movements other than heartbeats; (b)determining a periodic component of the output data from the motiondetecting sensor; (c) filtering the output data from the heartbeatwaveform sensor to remove variations that are slow with respect to anexpected heart rate, wherein the filtering produces high pass filteredoutput data from the heartbeat waveform sensor; (d) determining theuser's heart rate; and (e) presenting the user's heart rate.
 2. Themethod of claim 1, further comprising using the periodic component ofthe output data from the motion detecting sensor to remove acorresponding periodic component from the high pass filtered output datafrom the heartbeat waveform sensor.
 3. The method of claim 1, whereinthe filtering removes a frequency less than about 0.6 Hz from the outputdata from the heartbeat waveform sensor.
 4. The method of claim 1,further comprising determining the expected heart rate, and from theexpected heart rate, setting a high pass frequency for the filtering. 5.The method of claim 4, wherein determining the expected heart ratecomprises analyzing the output data from the motion detecting sensor. 6.The method of claim 5, wherein analyzing the output data from the motiondetecting sensor comprises inferring a user activity level.
 7. Themethod of claim 5, wherein analyzing the output data from the motiondetection sensor comprises detecting an intensity of user activity or atype of user activity.
 8. The method of claim 7, wherein detecting theintensity of user activity comprises detecting running or walking. 9.The method of claim 1, wherein the periodic movement detected by themotion sensor is a wearer's limb movements.
 10. The method of claim 1,wherein collecting the output data from the heartbeat waveform sensorcomprises: pulsing a light source in the worn biometric monitoringdevice at a first frequency; and detecting light from the light sourceat the first frequency.
 11. The method of claim 1, wherein presentingthe user's heart rate comprises presenting the heart rate on the wornbiometric monitoring device.
 12. The method of claim 1, furthercomprising determining the user's resting heart rate, wherein thefiltering removes a frequency less than about the frequency of theuser's resting heart rate.
 13. The method of claim 12, whereindetermining the user's resting heart rate comprises measuring the user'sheart rate soon after they wake up and are while still stationary inbed.
 14. The method of claim 12, wherein determining the user's restingheart rate comprises measuring the user's average heart rate of the userwhile the user is sleeping.
 15. A wearable fitness monitoring devicecomprising: a motion sensor configured to provide output correspondingto motion by a user wearing the fitness monitoring device; a heartbeatwaveform sensor; and control logic comprising instructions for: (a)collecting concurrent output data from the heartbeat waveform sensor andoutput data from the motion detecting sensor, wherein the output datafrom the heart beat waveform sensor provides information about theuser's heart rate and wherein the output data from the motion detectingsensor provides information about the user's periodic physical movementsother than heartbeats; (b) determining a periodic component of theoutput data from the motion detecting sensor; (c) filtering the outputdata from the heartbeat waveform sensor to remove variations that areslow with respect to an expected heart rate, wherein the filteringproduces high pass filtered output data from the heartbeat waveformsensor; (d) determining the user's heart rate; and (e) presenting theuser's heart rate.
 16. The wearable fitness monitoring device of claim15, wherein the heartbeat waveform sensor is a photoplethysmographicsensor comprising (i) a periodic light source, (ii) a photo detectorpositioned to receive periodic light emitted by the light source afterinteracting with the user's skin, and (iii) circuitry determining theuser's heart rate from output of the photo detector.
 17. The wearablefitness monitoring device of claim 16, wherein the photoplethysmographicsensor further comprises a housing having a recess in which the photodetector is disposed.
 18. The wearable fitness monitoring device ofclaim 15, wherein the control logic further comprises instructions forusing the periodic component of the output data from the motiondetecting sensor to remove a corresponding periodic component from thehigh pass filtered output data from the heartbeat waveform sensor. 19.The wearable fitness monitoring device of claim 15, wherein theinstructions for filtering comprise instructions for removing afrequency less than about 0.6 Hz from the output data from the heartbeatwaveform sensor.
 20. The wearable fitness monitoring device of claim 15,wherein the control logic further comprises instructions for determiningthe expected heart rate, and from the expected heart rate, setting ahigh pass frequency for the filtering.
 21. The wearable fitnessmonitoring device of claim 20, wherein the instructions for determiningthe expected heart rate comprise instructions for analyzing the outputdata from the motion detecting sensor.
 22. The wearable fitnessmonitoring device of claim 21, wherein the instructions for analyzingthe output data from the motion detecting sensor comprise instructionsfor inferring a user activity level.
 23. The wearable fitness monitoringdevice of claim 21, wherein the instructions for analyzing the outputdata from the motion detection sensor comprise instructions fordetecting an intensity of user activity or a type of user activity. 24.The wearable fitness monitoring device of claim 23, wherein theinstructions for detecting the intensity of user activity compriseinstructions for detecting running or walking.
 25. The wearable fitnessmonitoring device of claim 15, wherein the periodic movement detected bythe motion sensor is a wearer's limb movements.
 26. The wearable fitnessmonitoring device of claim 15, wherein the motion detecting sensor is anaccelerometer or a gyroscope.
 27. The wearable fitness monitoring deviceof claim 15, wherein the instructions for collecting the output datafrom the heartbeat waveform sensor comprise instructions for: pulsing alight source in the wearable biometric monitoring device at a firstfrequency; and detecting light from the light source at the firstfrequency.
 28. The wearable fitness monitoring device of claim 27,wherein the instructions for pulsing the light source at the firstfrequency comprise instructions for emitting a succession of lightpulses of substantially constant intensity.
 29. The wearable fitnessmonitoring device of claim 15, wherein the instructions for presentingthe user's heart rate comprise instructions for presenting the heartrate on the wearable biometric monitoring device.
 30. The wearablefitness monitoring device of claim 15, wherein the control logic furthercomprises instructions for determining the user's resting heart rate,and wherein the instructions for filtering comprise instructions forremoving a frequency less than about the frequency of the user's restingheart rate.